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Vision based wear monitoring of complex surfaces Maarten Vanoverberghe

Supervisors: Prof. dr. ir. Patrick De Baets, Prof. dr. ir. Mia Loccufier

Counsellor: dr. Ir. Jacob Sukumaran

Master's dissertation submitted in order to obtain the academic degree of Master of

Science in Electromechanical Engineering

Department of Mechanical Construction and Production Chairman:

Prof. dr. ir. Patrick De Baets

Department of Electrical Energy, Systems and Automation Chairman:

Prof. dr. ir. Jan Melkebeek

Faculty of Engineering and Architecture Academic year 2014-2015

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CASTIGAT RIDENDO MORES

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Acknowledgement

First and foremost I offer my sincerest gratitude to my supervisor, dr. Ir. Jacob Sukumaran, who has

supported me throughout my thesis with his patience and knowledge whilst allowing me the room to

work in my own way. I attribute the level of my Masters degree to his encouragement and effort and

without him this thesis, too, would not have been completed or written. One simply could not wish for

a better or friendlier supervisor. And I was very fortunate that he could laugh with my jokes.

Secondly I would like to thank my promoter’s prof. Patrick De Baets and prof. Mia Loccufier for making this

thesis possible and their support.

I also would like to thank all my friend who helped me in one or another way with my thesis. And my

companion Bavo who wrote a nice program for the setup.

Finally, I thank my parents for supporting me throughout all my studies at University and for providing

a home in which to complete my writing up.

Maarten Vanoverberghe

Gent, 2015

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Summary

Wind turbines are a booming business with the call for renewable energy. The number of new

installations of wind turbines are continuously increasing. Tribology of wind turbines is a major concern

because, the effect of wear and friction in the moving components are significant in-terms of cost and

down time. A monitoring system is required to understand the wear related challenges and to make

timely decisions on the maintenance of the moving parts. It is undeniable that regular service has to

be done to check the critical components for understanding the condition of the components. The

damage of critical components are identified from surface investigations on worn surface and relating

the observations to wear scars such as pitting, scuffing, etc. Such investigations are scheduled and the

service personnel performs the assessment. This is either subjective and also may be partly

complicated where physical intervention is required for inspection. A recently documented case on 29

October of 2013 reported with two causalities during a routine maintenance on a 67 meter high

turbine. Poor access to the critical components of the wind turbines and sometimes the turbine itself

(for eg. offshore turbines) proposes remote inspection system for damage detection. In existing

engineering practice, vibration sensing, temperature monitoring and proximity sensing is most

commonly used for damage detection. However, these are indirect methods for defect or wear

detection. A more appropriate and direct source for wear detection would be from the damaged

surface itself. Though several types are sensors are used, the final validation is done by the visual

inspection. Hence the present thesis investigates the possibilities for Remote Vision System (RVS) to

perform the assessment for damage detection at laboratory scale. Such a system could also be used

for early detection of wear in critical components of wind turbines. Early detection aids a time line

which can be made for maintenance and replacement of the broken parts. Thus with this system the

downtime of the wind turbine can be shortened. A cost saving of 20000 euro for maintenance and up

to 30000 euro for downtime (replacement of a gearbox) can be made. In this background the RVS was

developed on a laboratory scale. At this stage of the research testing scaled components might be

expensive and rather time consuming and hence a contact model of the critical components was

chosen. In our case, gears was chosen due to its criticality, poor availability, difficulty in identifying the

defects and downtime in maintenance of the gear box of the wind turbine. A close representative of a

gear contact is the twin-disc model which can experimentally simulate the rolling as well as the

combined rolling-sliding behavior of the gears. The RVS developed in this thesis was used in the twin

disc set-up.

The RVS consist of two main parts 1. Image acquisition and 2. Image processing. Considering the image

acquisition, the system has two important components the camera and the lens. The selection of the

camera sensor was carefully done using the EMVA1288 camera standard. Quantum efficiency,

sensitivity, noise, saturation capacity and dynamic range were considered for selection of the sensor.

Based on its high speed capabilities priority was given for CMOS (Complementary metal–oxide–

semiconductor) sensors. A 2/3”CMOSIS 2000 was then chosen sensor which has a resolution of 2048

x 1088 pixels, a field of view and a depth of view of 11 mm x 6 mm and 1.2 mm respectively. The lens

is a telecentric lens with a working distance of 65mm. This was chosen because larger working distance

allows monitoring of complex surfaces without any mechanical interference (for eample gears). In

regards to the illumination various illumination strategies were considered, out of which a bright field

near vertical illumination is used. The bright field illumination was chosen based on the test results

from preliminary investigations conducted on static surfaces. High intensity LEDs where used to build

the near vertical illumination where a specific pattern was used in the arrangement of LEDs for uniform

distribution. The RVS was then mounted on the test setup which was a modified FZG to perform the

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twin-disc tests. Surface defects such as pitting and scratches are seen as dark phases in the images

which are better recognized by the vision program.

The twin-disc tester experimentally simulated the rolling-sliding of gear systems where one of the disc

(D1) is driven by an electric motor. The second disc (D2) is a free rolling on its own axis and loaded

against the disc (D1). Thus the disc D2 is driven by the disc D1. Additionally, the test setup was then

further equipped with displacement sensor which measures the wear rate of the disc, a pyrometer is

used to measure the surface temperature of disc D1, a torque transducer, a friction sensor, two

vibration sensors to measure the force and vibration in the system respectively. The RVS is triggered

to acquire the image of same location at different instance of time. The trigger was fascinated by two

hall-effect sensors mounted to the shafts of the discs D1 and D2. Moreover, the hall-effect sensors

measure the rotations and speed from which the slip can be calculated. To control the machine and

record the information from the sensors LabVIEW was used. A national instrument 6036 E 16 bit multi-

function input output DAQ was used to read in the data from sensors. The camera system was directly

connected to the computer with USB 3.0 to have a rapid connection for the data to be transferred

from the camera. Two LabVIEW programs where simultaneously run for imaging system and other

sensors respectively.

This thesis also deals with the program for image acquisition and processing. The images are taken in

one burst without overlap and subsequently processed. First the images are saved and then queued

to the vision assistant for identifying surface defects. Different filtering methods such as edge

detection, thresholding and the Haralick features were used for change detection. The Haralick

features of the image are also calculated with the use of the co-occurrence matrix for understanding

the surface change. This methodology of image processing and acquisition enables us to acquire the

image in real time and process the same for instantaneous results. Most existing works defects

monitoring uses an offline processing where in the present work the processing of images are done

subsequently after image acquisition.

Different configuration from the view point of contact model, load, speed and environments were used

to validate the performance of the vision system. A Hertzian contact pressure ranging from 0.5 to 2

GPa was used. Tests were performed at dry and in presence of both oil and emulsion. The test results

clearly shows that surface defects and its evolution can be monitored in real time. The progress of the

surface defects can be well understood, where the present results from both the image and wear from

the displacement sensor reveals the dynamic characteristics of the wear process. The initially formed

pitting are removed as a function of time due to the partial slip. From the Haralick features and the

vision detection the pitting formation and grooving could then be detected with the program. The

newly developed RVS is appropriate to detect and monitor defect which can be remotely operated.

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Abstract

Wind turbines are a booming business with the call for renewable energy. The number of new

installations of wind turbines are continuously increasing. The effect of wear and friction in the wind

turbine components are significant in-terms of cost and down time. It is evident that the moving parts

which suffer wear may require appropriate monitoring system. Regular service has to be done to check

all the components for understanding the condition of the components by investigating the wear scars

such as pitting, scuffing, etc. Because of this automatic damage monitoring systems can have a

beneficial influence. In this thesis the implementation of such a system is being researched. The first

part is a literature review too see whether this system can have an economical benefit and where it

can be implemented and which damage occurs to the wind turbine. Here was found that the gearbox,

the pitch gears and yaw gears have the biggest potential and place to have the damage detection. This

on the pitch line of the gears where pitting takes place. Then some research is done for the camera

system. The camera sensor used is the CMOSIS 2000, the lens is telecentric with a working distance of

65 mm and a magnification of 1X. The last and one of the most important parts is the lighting solution

which is direct lighting with LED light. Ones the camera system is determined it is installed on the FZG

test setup where 2 disc are rotating to represent the fatigue wear on the pitch line of the gears of a

gearbox. By using LabVIEW a program is writing for the camera to start taking images at the same place

on the disc each time. This to make sure that the numbered images are matching for every burst. With

LabVIEW vision assistant the pitting was detected that occurred on the surface. With the co-occurrence

matrix other damage was seen such as abrasion. The system was than able to detect the start and

growth of the surface damage.

Vision based wear monitoring of complex surfaces

Maarten Vanoverberghe

Supervisors: dr. Ir. Jacob Sukumaran, prof. dr. ir. Patrick De Baets, prof. dr. ir. Mia Loccufier

Abstract: Wind turbines are a booming business with the call

for renewable energy. The number of new installations of wind

turbines are continuously increasing. The effect of wear and

friction in the wind turbine components are significant in-terms

of cost and down time. Regular service is an absolute must to

monitor the condition of the components and thereby

investigating the wear scars such as pitting, scuffing, etc. This is

tedious and rather subjective, hence an automatic damage

monitoring systems is foreseen and developed in the present

work. The remote vision system (RVS) is developed at laboratory

scale to understand its capabilities. The RVS carries contains two

main parts 1. Image acquisition and 2. Image processing. A

CMOS sensor in combination with the telecentric lens is used for

image acquisition. The second part which is the image processing

was done by means of the vision module of LabVIEW. Careful

consideration is given for the illumination where bring field

illumination is used. The newly built RVS is used in a twin-disc

set-up which represents a gear contact. The vision system

monitors the driven disc which is loaded against the driver. Tests

were performed in a lubricated environment where the

displacement of the shafts and the temperature of the contact

surface is measured together with the images. The evolution on

the change in contact surface was clearly observed with the RVS.

With LabVIEW vision assistant mechanisms such as pitting and

abrasion was detected. Amongst various image processing

techniques the dissimilarity in co-occurrence matrix revealed

profound information on the damage evolution. On the whole the

newly developed RVS stands effective at a laboratory scale

investigation of wear.

Keywords: Vision detection, pitting, wind turbine, gearbox

I. INTRODUCTION

All moving components suffer from wear which is also the

case for a wind turbine. Because of this maintenance engineers

need to the check the wear progression on the critical parts of

the machine. Different tools are used to check status of the

critical components. Amongst the difference possibilities the

vibration sensors and temperature measurements are often

considered to be effective. However, the status of the machine

can be understood from a vision system as well. For the wind

turbine this will be the gearbox, the yaw drive and the pitch

drive. These components exist out of gears where the surfaces

are accessible for the camera. The vision system then needs to

be selected from camera itself to the lens and illumination. All

these components are equally important to be able to detect

surface damage.

II. DAMAGE

Most wind turbines are designed to run at slow rotation

speeds. Because of this gearboxes are employed to transfer the

torque from 18 - 50 rpm to a high-speed output 1200 - 1800

rpm suitable for the generator [1].. The low-speed stage of the

gearbox is a planetary configuration with either spur or helical

gears. Depending upon their location within the gearbox, some

of the bearings and gears need to carry large loads at low

speeds while others carry lower loads at much higher speeds.

Since only one lubricant is used throughout the gearbox, the

lubricant film thicknesses will vary greatly from one location

to another. Because of the variability of wind conditions,

torque reversals from generator engagement can happen

thousands of times per year. Therefore during these events,

high-contact stresses are experienced by the gears, which can

result in wear and fatigue.

Figure 1 Left: Scuffing of the tooth and right: pitting at pitch line [2]

Between gear teeth a combination of rolling and sliding

takes place. The load is dynamic because the contact moves

over the tooth surface and also because the load is not constant

in size and direction.

Different failure modes take place such as scuffing or

scoring, spalling, pitting, wear, plastic flow and tooth fracture.

The most seen failures are scuffing, pitting and spalling [3].

Scoring or scuffing is due to combination of two distinct

activities: First, lubrication failure in the contact region and

second, establishment of metal to metal contact [3]. Pitting is a

surface fatigue failure of the gear tooth. This is shown in

figure 1.

III. VISION SYSTEM

Some different techniques were presented which includes

line scan camera, laser triangulation and area scan camera.

However, all these techniques vary in terms of accuracy,

characteristics, flexibility and rigidity. This is also due to the

fact the target application for measurements are different for

these versatile devices. The damage of the gears explained

before needs to be detected.

The chosen vision system is an area camera which takes a

picture of an area of the surface. This picture is then used for

further processing.

Camera selection

Choosing the suitable camera for a given machine vision

application often proves to be a challenging task. The

EMVA1288 standard which is the code from European

Machine Vision Association is used to develop a unified and

meaningful method of measuring a camera’s imaging

performance [4].

In the sensor selection priority is given for the Quantum

efficiency (QE) which is a measurement of the sensor’s ability

to convert photons to electrons. This can be seen on figure 13.

A sensor with higher quantum efficiency is better for low light

applications due to better conversion efficiency [5].

Figure 2 Working of camera sensor [6]

Sensors with a larger light sensitive area (larger pixel size)

will be exposed to more incoming photons and generate more

charge, leading to a higher saturation capacity. It means an

analog to digital converter will be able to convert the electrons

in to more grey levels, resulting in a higher dynamic range in

the captured image [5]. This saturation capacity and pixel size

can be seen on the figure above. The dynamic range of a

sensor is defined by the largest possible signal divided by the

smallest possible signal it can generate. The largest possible

signal is directly proportional to the saturation capacity of the

pixel. The lowest signal is the noise level when the sensor is

not exposed to any light, also called the "noise floor". A high

dynamic range camera will be able to produce enough detail in

the darker areas for the license plate to be recognized [4, 5].

A 2/3” CMOSIS 2000 was then chosen sensor has a

resolution (2048 x 1088), a field of view and a depth of view

of respectively 2048 x 1088, 11 mm x 6 mm and 1.2 mm. The

lens itself is a telecentric lens with a working distance of

65mm. This lens has large working distance which enables to

view complicated surface from a significantly long distance. In

regards to the lighting system various illumination strategies

were considered out of which a bright field near vertical

illumination is used. The bright field illumination was chosen

based on the results from preliminary tests conducted on static

surfaces. The pits and surface defects appear dark due to the

reflection of the photons away from the camera sensor.

Surface defects such as pitting and scratches seen as dark

phases in the images are better recognized by the vision

program. High intensity LEDs where used to build the near

vertical illumination where a specific pattern was used in the

arrangement of LEDs for uniform distribution. The whole

vision system was then mounted on the test setup which was a

modified FZG to perform the twin-disc tests.

IV. TEST SETUP

The test setup that is used for the thesis is a modified FZG

(Forschungsstelle für Zahnräder und Getriebebau). The setup

was rebuild as a twin disc setup where two disc can simulate

the contact at the pitch line of the gear contact where pure

rolling motion.

The setup has a pyrometer to measure the temperature of the

surface, a vibration sensor a torque sensor and an LVDT

sensor. Two hall-effect sensors are installed to measure the

rotations, the speed and the slip. Additionally these hall-effect

sensors aids to trigger the camera where images can be

acquired at the same location to monitor the evolution of wear.

A camera is then installed to check the surface for wear. On

the figure beneath a schematic representation is shown of the

test setup. Tests were performed in different ambient condition

such as dry, oil lubricated and emulsion supplied.

Figure 3 FZG test setup

V. LABVIEW PROGRAM

The vision acquisition block within LabVIEW is used. The

vision acquisition block has a standard framerate of 20 fps and

a resolution of 1920 by 1080p. The exposure time was set to

manual and adjusted to get a good image. Because of the

bright LED lights the exposure time can be set very low and

because of this the speed while taking images could be

relatively high.

For the vision detecting Threshold conditioning was used.

After that the image was filtered to only have the pitting. The

processing can be seen below

Figure 4 Flow chart vision based pitting detection

The effect on the image can then be seen on figure 5 where

at the end only the pitting is left and can then be used as a

value for the damage on the surface.

Figure 5 Pitting detection with LabVIEW

Original

Lumunance

Threshold

Filter smal objects

Fill holes

VI. TEST RESULTS

In the first test pitting was found. Images of this were used

to make the LabVIEW program as seen above. The pitting can

be compared with the literature where it starts at a 30° angle,

then goes to a 45° angle and then goes almost straight up. This

can be seen on figure 6.

Figure 6 Making of surface pitting [7]

In the last test some pitting was found. In the evolution it is

clear that the pitting which was found in the course of time

disappear due to the abrasion from partial sliding. This can

nicely be seen on the images where between two images of the

same place changes can be seen such as debris that disappear.

An example is shown on figure 7.

Figure 7 Disappearance of debris after 20 min

These debris which was further collect from the oil circuit

has a pattern similar to the surface defect. The debris exist of

small particles from pitting and long particles which peels off

due to the abrasive wear (see figure 8)

.

Figure 8 Wear debris of the last test

Apart from the wear process the corrosion was also

observed from the newly developed vision system. Surface

observations on the tests performed at dry contact shows

discoloration of the surface.

Figure 9 Graph of % area of pitting on the surface

The peaks in the beginning are because of the fact that the

light was not reflected directly into the camera. This can be

seen on figure 9. The images are taken at the same place but

due to the different reflection of the light appears darker.

The following high values and lower ones are because of this

reason. The lower peaks found are when the light is again

reflected straight into the camera. This can be seen on the

image at the low peak just after 10 hours (180 000 cycles).

Between 32 and 55 hours the pitting percentage is also very

low. This because the surface is smoothened out by the

abrasive wear as seen on image at 40 hours. The value at 40

hours (750 000 cycles) is not equal to zero because of the fact

that on other images of the burst pitting is found. After 65

hours (1 400 000 cycles) the surface damage increases rapidly.

This can also be seen on the images where surface damage is

increasing a lot. Because of the wearing of the damage and

falling off of the debris the surface damage area is declining.

Beside the pitting detection the co-occurrence matrix was also

used to look at the surface damage. The different Haralick

features of the images were checked. These are the contrast

the dissimilarity, the correlation, the homogeneity and the

entropy. The graphs of these images are then also compared

with the taken pictures.

VII. CONCLUSION

The newly developed Remote Vision System (RVS) system

was developed to check the surface damage. The RVS stands

effective in the twin-disc model and also in the oil lubricated

environment. Evolution of contact surface from the view point

of abrasion and pitting was evident. Dynamic behavior of the

surface defects was clearly monitored using the visio0n

system. The system is effective at a laboratory scale for which

more experiments can be planned to perform damage

propagation studies.

REFERENCES

1. H. Link, J.K., and Y. Guo, Gearbox Reliability Collaborative

Phase 3 Gearbox 2 Test Plan. 2013, national laboratory of the

U.S. Department of Energy: osti.

2. novexa. Gear Defects treated. 2014 [cited 2014 16/11/2014];

Available from: http://www.novexa.com/en/engrenage-

defauts.php.

3. Beek, A.v., advanced engineering design. 2012.

4. Association, E.M.V., Standard for Characterization of Image

Sensors and Cameras. 2010.

5. grey, p. Sony Pregius Global Shutter CMOS Imaging

Performance. Sony's Latest Sensor Technology for Exceptional

Imaging Performance 2014 [cited 2015 3/02/2015]; Available

from: http://www.ptgrey.com/white-paper/id/10795.

6. Tucakov, V., How to select the right camera using the EMVA

1288 imaging performance standard. Point grey, 2014: p. 29.

7. J.E. Fernandez Rico, A.H.B., D. Garcia Cuervo, Rolling contact

fatigue in lubricated contacts. Tribology International, 2002. 36:

p. 35-40.

45°

30°

Contents

1. Wind Turbine ................................................................................................................................... 1

1.1. Introduction ............................................................................................................................. 2

1.1. Overview of parts .................................................................................................................... 2

1.1.1. Main overview ................................................................................................................. 2

1.1.2. Rotor ................................................................................................................................ 3

1.1.3. Blade Pitch and Yaw system ............................................................................................ 3

1.1.4. Gearbox ........................................................................................................................... 4

Environment ................................................................................................................................ 5

1.1.5. Main shaft bearing .......................................................................................................... 5

1.2. Running and maintenance ...................................................................................................... 6

1.2.1. Maintenance .................................................................................................................... 6

1.2.2. Downtime ........................................................................................................................ 7

1.3. Costs ........................................................................................................................................ 8

1.4. The commonly used technique to understand the failure mode ........................................... 9

1.4.1. Overview .......................................................................................................................... 9

1.4.2. Wear debris analysis ...................................................................................................... 10

1.4.3. Vibrations ...................................................................................................................... 10

2. Gearbox Failure Modes and Failure Mechanisms ......................................................................... 12

2.1. Wear defect ........................................................................................................................... 13

2.1.1. Scoring ........................................................................................................................... 14

2.1.1.1. Abrasion ................................................................................................................. 14

2.1.1.2. Adhesion ................................................................................................................ 14

2.1.2. Pitting ............................................................................................................................ 15

Sliding rolling contact fatigue .................................................................................................... 16

2.1.3. Fatigue ........................................................................................................................... 17

2.1.4. Teeth defect .................................................................................................................. 17

2.2. Defect size ............................................................................................................................. 18

3. Materials and Methods ................................................................................................................. 19

3.1. Tribological model ................................................................................................................. 20

3.1.1. Description of the experimental setup.......................................................................... 20

3.1.2. Lubrication ..................................................................................................................... 23

3.2. Selection of experimental condition for pitting detection .................................................... 24

3.3. Material, contact pressure, speed, time ............................................................................... 25

3.3.1. Material of gears in wind turbine .................................................................................. 25

3.3.2. Contact pressure wind turbine ...................................................................................... 25

3.3.3. Design of experiments ................................................................................................... 26

4. Remote Vision System ................................................................................................................... 27

4.1. Remote Vision System (RVS) ................................................................................................. 28

4.1.1. Line scan camera ........................................................................................................... 29

4.1.2. Laser triangulation ......................................................................................................... 29

4.1.3. Machine vision with area camera ................................................................................. 30

4.2. Camera selection for the RVS ................................................................................................ 30

4.2.1. CMOS vs CCD ................................................................................................................. 30

4.2.2. Shutter Technology ....................................................................................................... 31

4.3. Explanation ISO norm ............................................................................................................ 32

4.3.1. Quantum efficiency ....................................................................................................... 32

4.3.2. Read Noise ..................................................................................................................... 33

4.3.3. Saturation Capacity & Dynamic Range .......................................................................... 33

4.3.4. Sensitivity ...................................................................................................................... 33

4.3.5. Compering different sensors ......................................................................................... 34

4.4. Selection of lens .................................................................................................................... 35

4.5. Illumination ........................................................................................................................... 37

4.5.1. Strobe lighting vs continuous lighting ........................................................................... 40

Continuous lighting ................................................................................................................... 40

Strobe lighting ........................................................................................................................... 40

4.6. Remote Vision System (RVS) ................................................................................................. 41

5. Image Acquisition And Processing Using LabVIEW ....................................................................... 42

5.1. Theory behind vision detection ............................................................................................. 43

5.1.1. Image acquisition .......................................................................................................... 43

5.1.2. Different analysis systems ............................................................................................. 43

5.1.3. Threshold with conditioning .......................................................................................... 44

5.1.4. Grey-scale Co-occurrence Matrix .................................................................................. 45

5.1.4.1. Almost no surface damage .................................................................................... 47

5.1.4.2. Medium surface damage ....................................................................................... 47

5.1.4.3. Major surface damage ........................................................................................... 48

5.1.4.4. Comparison............................................................................................................ 48

5.1.5. Edge detection ............................................................................................................... 49

5.1.5.1. LabVIEW solutions ................................................................................................. 49

5.1.5.2. Sobel ...................................................................................................................... 50

5.1.5.3. Perwitt ................................................................................................................... 50

5.1.5.4. Further processing ................................................................................................. 51

5.2. Standalone LABVIEW program for twin-disc image acquisition and processing .................. 51

5.2.1. User interface ................................................................................................................ 51

5.2.2. Program ......................................................................................................................... 55

6. Test Results .................................................................................................................................... 59

6.1. Results dummy tests (test 0) ................................................................................................. 60

6.2. First test complete setup ....................................................................................................... 64

6.3. Second test ............................................................................................................................ 66

6.4. Third test ............................................................................................................................... 69

6.5. Fourth test ............................................................................................................................. 73

6.5.1. Initial information .......................................................................................................... 73

6.5.2. Start of test .................................................................................................................... 73

6.5.3. Test Images .................................................................................................................... 74

6.5.4. Program calculations results ......................................................................................... 77

6.5.4.1. Surface morphology .............................................................................................. 77

6.5.4.2. Haralick features .................................................................................................... 78

6.5.5. Other sensors ................................................................................................................ 79

6.5.5.1. Measurements....................................................................................................... 80

7. Conclusion and Future Work ......................................................................................................... 83

7.1. Conclusion ............................................................................................................................. 84

7.2. Future work ........................................................................................................................... 84

7.3. References ............................................................................................................................. 85

8. Appendix ........................................................................................................................................ 88

8.1. Appendix A: Test protocol ..................................................................................................... 89

8.2. Appendix B: Vision system specifications.............................................................................. 90

8.2.1. Camera .......................................................................................................................... 90

8.2.1.1. Camera .................................................................................................................. 90

8.2.1.2. Sensor .................................................................................................................... 90

8.2.1.3. Temperature .......................................................................................................... 91

8.2.1.4. Speed of camera .................................................................................................... 91

8.2.2. Lens ................................................................................................................................ 91

8.2.3. Lighting .......................................................................................................................... 92

8.2.3.1. LED light ................................................................................................................. 92

8.2.3.2. Strobe control ........................................................................................................ 93

8.3. Appendix C: Sensor ................................................................................................................ 95

8.4. Appendix D: Lens ................................................................................................................... 97

8.5. Appendix E: Contact pressure calculations ........................................................................... 98

8.5.1. Cylinder on cylinder ....................................................................................................... 98

8.5.2. Elliptic contact ............................................................................................................. 100

8.6. Appendix F: Surface roughness ........................................................................................... 101

1

Chapter 1

1. Wind Turbine

2

1.1. Introduction Wind energy generation has been in practice for more than 4000 years [3]. In 1700 B.C. wind power

was used to irrigate Mesopotamia. The Persians used the wind energy for grinding where the food

grains are converted to flour, while others used the wind to transport (sails) armies and goods across

oceans and rivers. The usage of wind energy to propel boats was already there in 5000 B.C. A more

recent application is for pumping water and producing electricity [3].

Producing electricity with wind turbines dates back to the end of the last century. Initially it was used

by farmers to have local electricity generation. Due to the oil crises in the 1970s and with the increasing

price for fossil fuel wind power gained its importance in energy generation. Since 1980, wind power

has been the fastest growing energy technology in the world. This can be seen on Figure 1 where the

total capacity of wind energy has reached 238 GW in a span of 15 years [4]. The preference given for

the wind energy generation has brought new and innovative solutions. The innovation in the wind

energy can be recognised from the new design, materials used in critical components and the

monitoring system. Efficient operation of the turbine relies on number of critical components.

Figure 1 Global installed wind power capacity 1996 to 2011 [4]

1.1. Overview of parts

1.1.1. Main overview

A wind energy conversion system primarily consists of a turbine tower which carries the nacelle, and

the wind turbine rotor, consisting of rotor blades and the hub. The tower is one of the most imported

part of the wind turbine. The higher the tower the stronger the wind will be at higher altitudes. There

are limitations to the height from the view point of weight and the environment. There are many

different types of towers available for a wide variety of turbine sizes. The most used type is the tube

tower. It consists of a single pole that supports the turbine [3]. Most modern wind turbines are

horizontal-axis wind turbines with three rotor blades usually placed upwind of the tower. Figure 2

shows the major components of a wind turbine. In most of the turbines the generator input shaft

rotates at 1600 rpm [5]; thus it is accommodated with a multi-stage gearbox consisting parallel and

planetary gear stages. The rotational speed of the blades goes from 5 to 20 rpm. In addition to the

gearbox a wind turbine contains several other moving parts which is prone to wear such as bearings

and gears. Few of these components are main rotor shaft bearings, generator bearings, and the

bearings and gears used for the blade pitch and yaw drives. The nacelle contains all the machinery of

the wind turbine, i.e. the drive train including the mechanical transmission (rotor shaft, bearings and

the gearbox) and the electrical generator. Additionally other equipment such as the power electronic

interface, yaw drive, mechanical brake, and control system are also accomodated [4].

3

Figure 2 The major components of a Wind turbine [3] Figure 3 Illustration of a direct drive wind turbine [6]

Newer turbine architectures have been developed in recent years that eliminate the gearbox and

connect the main shaft directly to a permanent magnet generator. This is an advantage because the

gearbox is one of the main components that suffers wear.

1.1.2. Rotor

The rotor is the heart of a wind turbine and consists of multiple rotor blades attached to a hub. It is

the turbine component that is responsible for collecting the energy present in the wind and

transforming this energy into mechanical motion. Today, most rotors have three blades, a horizontal

axis where the diameter ranges between 40 and 100 meters. The use of three blades is because of the

fact that this creates a better distribution of mass which allow smoother rotation [4].

1.1.3. Blade Pitch and Yaw system

Different control methods can be used to either optimize or limit the power output at specific wind

speed or direction. The turbine can be controlled by regulating the generator speed, blade angle

adjustment, and rotation of the entire wind turbine.

Figure 4 Motor drive pitch and yaw [8]

Standard modern turbines usually pitch the blades at high winds in order to operate within safe

rotational speeds. This precisely controls the blade angle thereby withstanding high torque loads [8].

In Figure 5 there is an example of a motor for Pitch control. The blade angle is always optimally adjusted

to the wind speed through the pitch bearing in order to control the performance of the wind turbine.

The high loads from the dynamic stresses on the rotor blades must be safely dissipated into the rotor

4

hub via the raceways and the screw connections [3]. A robust slewing ring is necessary to adjust the

position of the nacelle in the wind direction. The wind force and the dynamic inertial forces are

dissipated safely into the tower head through the raceways and screw connections.

Figure 5 Right: Yaw control bearing and gear motors. Left: Pitch bearing [8]

1.1.4. Gearbox

Most wind turbines are designed to run at slow rotation speeds. Because of this gearboxes are

employed to transfer the torque from 18 - 50 rpm to a high-speed output 1200 - 1800 rpm suitable for

the generator. A schematic of a wind turbine gearbox is shown in Figure 6. The low-speed stage of the

gearbox is a planetary configuration with either spur or helical gears.

Figure 6 Gearbox wind turbine [7]

Gear box being the most critical component in wind turbine priority was given to study and monitor

the failure of the gears and to understand the failure mechanisms [5]. Before installing the equipment,

the installation space needs to be evaluated. The possibility of aligning the illumination and the camera

has to be checked. This put forwards a question that if an insight into the inspection scene is possible?

What variations are possible for minimum and maximum distances between the part and the camera?

As explained before the bearing don’t have enough insight to be examined via machine vision. The

gear driving system of the pitch and yaw system are clearly visible (see figure 5) and a vision system

can be installed for monitoring. The gears in the gearbox could also be monitored if enough space is

available for mounting of the camera and lens. From the picture (see Figure 7) it is evident there is

enough room to install the vision system.

5

Figure 7 Reconditioned gearbox of wind turbine [9] Figure 8 View inside a Liberty wind turbine gearbox [10]

Environment

The installation environment of the system is also very important following things need to be checked:

Ambient light;

Dirt or dust that the equipment needs to be protected from;

Shock or vibration that effect the image;

Operational temperature;

Lubricated or dry contact condition

Power supply.

1.1.5. Main shaft bearing

The rotor shaft bearing supports the blades and rotor and transmits torque to the gearbox. The bearing

loads and rotating speeds vary considerably due to constantly changing winds [11]. Since rotor shaft

bearings are exposed to vibration of blades and gearbox, fretting corrosion may occur. Therefore

selection of appropriate bearings and grease, as well as optimization of clearance and fitting are

important factors [11].

Figure 9 Typical wind turbine main shaft spherical roller bearings support the load on the downwind row [6]

A huge challenge for vision examinations is the accessibility to the damaged area. The raceways of the

bearings are mostly not accessible. From Figure 10 it can be clearly seen that there is no access for the

camera for implementing a vision system.

6

Figure 10 Bearings of the Rotor shaft and the rotor shaft installation [9, 12]

1.2. Running and maintenance

1.2.1. Maintenance

Maintenance strategies is challenging in the operation of wind turbines. It can be organised in reactive and preventive strategies. Reactive is when some damage already occurred and then the maintenance procedure is then carried out. Preventive are more sophisticated and reliability based maintenance. An example of both is shown in Figure 11.

Figure 11 Maintenance example of a system [13]

Both systems aim at finding the optimum point in time for carrying out the required maintenance

actions. The condition based maintenance (CBM) tries to find the optimal point by monitoring the

current status of the components. This can be seen as the blue shaded area of Figure 11 [13]. This is

the point where the wear of the machine is at a specific predetermined point where interventions are

required. To locate this region in the life time operations on has to monitor the critical sections. While

the objective of preventive maintenance or reliability based maintenance is to replace components

and refurbish systems that have defined useful lives, usually much shorter than the projected life of

the turbine. Tasks associated with scheduled maintenance fall into this category.

The baseline maintenance costs for a 2MW wind turbine were taken as 10000 euro per year [14]. If 6-

monthly maintenance is adopted, this corresponds to 5000 euro per maintenance action. Therefore

the (planned) maintenance costs of a condition based monitoring policy can be calculated as a yearly

proportion, depending on the frequency of maintenance actions [14]. It can be foreseen that

implementing a vision based monitoring system in CBM can be used to reduce the maintenance costs

significantly.

7

1.2.2. Downtime

A list of failures from various wind turbines parts are listed in Figure 12, which are due to wear out,

relaxation, storm events as well as others and unknown causes. The annual downtime of the affected

subassemblies is shown in Figure 12. The date on the figure is failure and downtime from 2 large

surveys of onshore European wind turbines: period 1993-2006.

Figure 12 Most severe failures and downtime [14]

It is clear through this dialogue that the most significant operational failures are associated with the

gearbox, the drive train, the rotor blades and generator components. The reasons for this high

significance can be summarised:

High capital cost and long lead-time for replacement;

Difficulty in repairing in-situ;

Large physical size and weight;

Position in nacelle at top of tower;

Lengthy resultant downtime, compounded by adverse weather conditions.

The final point can be reinforced when it is understood that typical downtime for a gearbox

replacement may be around 600 hours (25 days) according to Ribrant and Bertling, while industrial

sources suggest a figure of 700 hours (29 days). Utility operational experience indicates that this figure

is highly dependent on availability of a spare gearbox: on one hand a replacement could be carried out

in 7 days if a spare were available, whereas long component lead times could result in a delay of up to

60 days in some cases [14].

Some maintenance and repair actions are subject to weather constraints, set by the owner/operator

for health and safety reasons. Such constraints can represent a problem to the operator, since

maintenance activities will obviously have to be inhibited in certain weather conditions such as high

winds. The Table 1 shows a set of constraints employed by a utility with an extensive wind farm

operations portfolio: these are readily accommodated within the maintenance model [14].

8

Table 1 Maintenance Constraints [14]

1.3. Costs David McMillan & Graham Ault made a comparison between a 6-monthly periodic maintenance policy

and a condition based policy. The baseline maintenance costs for a 2MW WT were taken as £10K per

year. If 6-monthly maintenance is adopted, this corresponds to £5K per maintenance action. Therefore

the (planned) maintenance costs of a CBM policy can be calculated as a yearly proportion, depending

on the frequency of maintenance actions [14]. One of the central points of interest is how the revenue

streams of the two approaches compare. While comparing the periodic maintenance against the

condition monitoring based. A mean annual revenue value of just over 29000 euro per year for

condition-based maintenance, relative to the widely-used periodic maintenance policy [14]. The

confidence limits indicate a minimum annual benefit of around 20000 euro. Assuming a post-warranty

useful life of 15 years, this represents total operational savings of 300 000 euro per turbine [14]. Figure

13 shows the percentage of CBM benefit attributable to lost energy if function of the downtime and

capacity factor.

Figure 13 % of CBM Benefit Attributable to Lost Energy [14]

Every wind turbine has a range of wind speeds, typically around 48 to 88 km/hr, in which it will produce

at its rated, or maximum, capacity. At slower wind speeds, the production falls off dramatically. If the

wind speed decreases by half, power production decreases by a factor of eight. On average, therefore,

wind turbines do not generate near their capacity. Industry estimates project an annual output of 30-

40%, but real-world experience shows that annual outputs of 15-30% of capacity are more typical [14,

15].

With a 25% capacity factor, a 2-MW turbine would produce:

2 𝑀𝑊 𝑥 365 𝑑𝑎𝑦𝑠 𝑥 24 ℎ𝑜𝑢𝑟 𝑥 25% = 4380 𝑀𝑊ℎ

A capacity of 50 % is taken for one day of working:

9

2 𝑀𝑊 𝑥 24 ℎ𝑜𝑢𝑟 𝑥 25% = 12000 𝑘𝑊ℎ

The revenue from the production of the wind powered electricity exist out of two parts. The energy

price for selling at the moment and the subsidies on renewable energy. This is nowadays respectively

5 eurocent/kWh and 9 eurocent/kW. Which comes to 14 eurocent per kWh [16]. With the price of 14

eurocent per kWh this can go to 1680 euro/day.

Having a delivery time of a new gearbox are around 29 days as explained before:

1680𝑒𝑢𝑟𝑜

𝑑𝑎𝑦𝑥 29 𝑑𝑎𝑦𝑠 = 48720 𝑒𝑢𝑟𝑜

1.4. The commonly used technique to understand the failure mode

1.4.1. Overview

Figure 14 show the detection phases in a wind turbine failure. The earlier the damage is detected the

better the machine state still is and the less money it will cost to repair. If detection is on the early

stages of wear the replacement part can be ordered and a schedule can be made. Figure 14 shows that

particle detection can be done at an earlier stage than the vibrations. The history of detection

techniques is listed below.

Figure 14 Damage detection monitoring [17]

Turn of 20th century: Fluids Temperature (Oil or Coolant) and Oil Pressure;

Early 20th century: Oil Analysis for physical properties (e.g. Viscosity, solids, acids, water,

glycol, etc.);

1948: Oil Analysis for wear metals. This was a seminal moment in CM because it marked the

first time that machine condition was the primary objective for oil analysis;

1960: Viability of commercial Oil Analysis laboratories when a semi-automated spectrometer

for elemental measurement was invented;

1970: Vibration Analysis. This tool’s introduction, like oil analysis in 1960, ushered in a new

capability: onsite performance monitoring;

1980: Thermographic Imaging for damage detection;

1990: Acoustic Signatures;

Early 21st century: Online sensors for monitoring lubricant and machine health indication

parameters. [18]

10

1.4.2. Wear debris analysis

The lubricant flows to a filter where the wear particles get filtered out and can be analysed. This was

done by optical counting prior to 1970 later on vision systems were developed to find the particles

there geometric characteristics and there size. Figure 15 shows the concentration and size of the

particles generated in time. Vision systems have been developed that use cameras to detect the

particles and their size and shape [18]. With this analysis direct and early information on wear modes

and the condition of the machine can be provided.

Figure 15 Particle size of wear debris [17]

Figure 16 shows the five types of wear particles found in a worm gear test where the viscosity of the

test oil was too low. Rubbing, Cutting, laminar, sliding and fatigue particles were present and this with

a size of approximately 100 µm. At the contact surface a rough surface was found.

Figure 16 Wear Particles Generated from a Worm Gear test [19]

1.4.3. Vibrations

Vibration analysis in particular is a popular predictive maintenance procedure. As a general rule,

machines do not break down or fail without some form of warning, which is indicated by an increased

vibration level. By analysing the vibrations it is possible to determine the nature and the severity of

the defect. Depending on vibrations at different frequencies specific machine faults can be detected.

However this can also be used for:

Identifying misalignment at installation or anytime thereafter;

Identifying imbalance conditions;

Isolating a trauma spot when there are several identical mechanical locations where oil

analysis indicates possible problems with wear;

Live, real-time online monitoring.

11

As an example Figure 17 shows a narrowband region of increasing energy content around 260 Hz to

280 Hz. This region represents the bearing defect. The other peaks already where identified at the

beginning of the process.

Figure 17 Frequency spectrum of a machine monitoring system [19]

12

Chapter 2

2. Gearbox Failure Modes

and Failure Mechanisms

13

2.1. Wear defect Life expectancy of mechanical systems is always dependent on the most critical component of the

system. In power transmissions this is usually the gear. Gear design is commonly bounded by the

requirement that the gear should carry high loads at high speeds with minimal size and weight [20]. In

Figure 18 the different failure modes of gears are clearly illustrated.

Figure 18 Different modes of gear failure [21]

Wear is primarily a phenomenon caused by the relative movement between the surfaces. Wear can

be generated either from relative sliding or rolling of surfaces against each other. Generally, the wear

process has three regimes, (I) running-in which is relatively short, (II) the second region is the

stationary wear and the third stage (III) is where severe wear occurs [22]. All this can be seen on figure

19. Since the second region has a linear wear trend design calculations for wear life time operations

can be performed using simplified models. As mentioned above different types of wear mode persist

which can be classified into mild and severe wear. To understand more about the wear a clear in-depth

knowledge of the wear process is required. To understand the wear process the wear mode, wear

mechanism and debris are often examined. A typical methodology in wear investigation is the wear

mechanism both at microscopic and macron scale. The mechanism provides sufficient information on

the wear process and also on the condition of the component by indicating the severity of the

mechanism. To have a better understanding the various wear mechanisms are discussed below.

Figure 19 Time-dependent wear of a tribological system [22]

14

2.1.1. Scoring

Scoring is due to combination of two distinct activities: First, lubrication failure in the contact region

and second, establishment of metal to metal contact. Later on, welding and tearing action resulting

from metallic contact removes the metal rapidly and continuously as long as the load, speed and oil

temperature remain at the same level. The scoring is classified into initial, moderate and destructive.

Scuffing and scoring are frequently interchanged.

Figure 20 Left: Scuffing or scoring at the top of the tooth [23]

2.1.1.1. Abrasion

Abrasion is the process of scuffing, scratching, wearing down or rubbing away of material. Abrasion

process involves ploughing of soft surface by hard asperities. With abrasion, phenomena such as

ploughing and cutting persist. This cause geometrical changes to the surface and introduce material

loss which leads to scars. The most dominant scars are those following the sliding direction caused by

ploughing or cutting. Zhum Gahr classify abrasive wear with four different phenomena [24]:

1. Ploughing;

2. Cutting;

3. Fatigue;

4. Cracking.

Abrasion can occur in two different ways:

1. Two body abrasion;

2. Three body abrasion.

Figure 21 Abrasive wear two- and Three-body Abrasion [25]

Three-body abrasion occurs when a relatively hard contaminant (particle of dirt or wear debris) of

roughly the same size as the dynamic clearances becomes embedded in one metal surface and is

squeezed between the two surfaces, which are in relative motion [25]. Wear mechanisms that take

place at the interface between two contacting bodies are classified as two-body wear mechanism. A

form of this arises when free solid particles between surfaces contribute to the wear process. Four

fundamental two-body wear mechanisms can be distinguished: adhesive wear, abrasive wear,

corrosive wear and surface fatigue [25].

2.1.1.2. Adhesion

Adhesion wear occurs when strong adhesive bonding between interacting asperities causes micro-

welding. This is due to atomic or inter-molecular forces. In other words a process in which the junctions

shear off during the course of interaction between two bodies. Such mechanisms leave scars of the

fragmented region and adhered debris in the counter face material. [26]

15

Figure 22 Adhesive wear [25]

2.1.2. Pitting

The most common mode of gear failure encountered in practice is that of surface contact fatigue. This

mode of failure leads to crack initiation at or near the contact surface, and may subsequently lead to

damage varying in extent from microscopic pitting to severe spalling. The metal removed from the

surface in such cases enters the machine system, and can, in turn, cause three body abrasive wear and

failure of other components. Furthermore, the pits formed on the damaged surface lead to the

formation of stress concentrations [27, 28].

Examination of pitted gear teeth indicates that pitting originates in a predictable region of a gear teeth

face. This lies roughly between the initial point of a single tooth pair contact and the pitch line of the

gear. Pitting is expected to occur first on the high speed pinion due to the higher number of revolutions

and hence higher number of teeth contacts relative to the gear [20]. When both engaging gears are

made of the same material, the pitting is expected to occur first on the smaller gear [28]. The damaged

region spans roughly from the line corresponding to the initial point of a single teeth pair contact to

the line defined by the pitch circle of the gear. In this region the gear tooth surface is subjected to the

highest contact forces, dynamic effects and the damaging action of opposite sliding and rolling

velocities [28, 29].

Whenever two curved (usually convex) surfaces are in contact under load, the contact occurs along a

line or point, or, depending on the elastic constants of the materials concerned, along a very small

circular or elliptical area. As a result of such small contact areas, the shear (Hertzian) stresses which

develop at and near the surface are consequently very high. The maximum shear stress occurs at some

distance below the surface, as illustrated in the figure below [27]. Pitting under pure rolling can occur

even under proper lubrication conditions, since oil, as an incompressible fluid, will merely transmit the

contact loads.

Figure 23 Stress distribution at and near two contacting surfaces [27]

16

Sliding rolling contact fatigue

Pure rolling conditions prevail when the surface velocities of two contacting curved bodies are the

same. However, if these velocities are different, an element of sliding is introduced which significantly

alters the stress distribution in the surface and near-surface material. Gear teeth have complex

combinations of sliding and rolling, which vary along the profile of each tooth, as illustrated in Figure

24.

Figure 24 Combination of sliding and rolling in gear teeth [27]

The complete fatigue process leading to pitting can be split into the period required for the fatigue

crack initiation and the period needed for the fatigue crack propagation from the initial to the critical

crack length. It occurs due to repeated loading of tooth surface and the contact stress exceeding the

surface fatigue strength of the material. Material in the fatigue region gets removed and a pit is

formed. The pit itself will cause stress concentration and soon the pitting spreads to adjacent region

till the whole surface is covered. Subsequently, higher impact load resulting from pitting may cause

fracture of already weakened tooth. However, the failure process takes place over millions of. In

surface-hardened gears, the variable stresses in the underlying layer may lead to surface fatigue and

result in flaking (spalling) of material. The two classes of pitting are subsurface or surface origin failure

shown in Figure 25 [21].

Figure 25 Subsurface origin failure and surface origin failure [21]

Individual micro pits are not visible to the unaided eye, however, many micro pits normally appear

together resulting in a dull matted area. On gear teeth, the tooth surface often must be illuminated

from various angles to observe micropitting.

17

Figure 26 Spalling phenomenon on gear teeth flanks [28] Figure 27 Progressive pitting on gear [23]

2.1.3. Fatigue

The other wear mechanisms mainly occur between sliding surfaces. Surface fatigue is generally the

predominant wear mechanism in a rolling contact. It is also one of the passive modes of particle

removal which requires substantial amount of time to produce visible wear scars on the surface level.

The pressure distribution is not smooth but in reality made up of multiple of high pressure zones. In

the subsurface region the stresses are similar to those of smooth macro contact. After repeated

loading wear particles can be generated leaving small ‘pits’. This is known as micropitting. In the case

of rolling with traction, the high stressed area lies even closer to the surface. With high stresses the

critical stress occurs at the surface causing cracks to start there. These may propagate further. The

common fatigue phenomenon in steel is pitting and spalling which manifest as craters on the contact

surface.

Table shows the wear mechanism and the manifestation of various phenomenon and its sizes. This

enables us to select an appropriate monitoring system to be implemented so that these mechanisms

and surface defects can be identified even at the beginning of the wear process.

Mechanism Type of scars Scar size

Abrasion Grooves 10 µm wide

Adhesion Craters/transferred materials 10 µm wide

Fatigue wear Pits/craters 10 – 1000 µm

Table 2 Damage sizes and scars [29]

2.1.4. Teeth defect

Plastic flow of tooth surface results when it is subjected to high contact stress under combined rolling

/sliding action. Surface deformation takes place due to yielding of surface or subsurface material.

Normally it occurs in softer gear materials. But it can occur even in heavily loaded case hardened gears.

A reason for this can be due to overheating which is associated with insufficient lubrication.

Tooth fracture is the most dangerous kind of gear failure and leads to disablement of the drive and

frequently to damage of other components (shafts, bearings, etc.) by pieces of the broken teeth. Tooth

breakage may be the result of high overloads of either impact or static in nature, repeated overloads

causing low-cycle fatigue, or multiple repeated loads leading to high cycle fatigue of the material [21].

18

2.2. Defect size Surface pitting of gear teeth flanks is one of the most common causes of gear operational failure. The

examination of real gear teeth indicates that surface pitting is usually restricted to a particular region

of a gear tooth surface. The damaged region spans roughly from the line corresponding to the initial

point of a single teeth pair contact to the line defined by the pitch circle of the gear. In this region the

gear tooth surface is subjected to the highest contact forces, dynamic effects and the damaging action

of opposite sliding and rolling velocities. When both engaging gears are made of the same material,

the pitting is expected to occur first on the smaller gear, which has the higher number of revolutions

and hence the higher number of relative teeth contacts per gear [28]. In the results of experimental

studies done by K. Aslantas and S. Tas, the pitting failure size observed on the tooth surface changes

between 150 and 500 µm at the pitch line. Gear materials are generally subjected to heat treatment

to prevent surface fatigue failures such as pitting or spalling [29].

Figure 28 SEM micrograph of pitting failure on gear tooth surface. Left: austempered and right: as-cast gear material [29]

It can be seen on the figures that the fractured surfaces of the pitting failures have irregular

morphology. Figure 28 also show the subsurface crack propagation due to cyclic contact loadings in

black zone indicated with arrows.

Figure 29 Experimentally obtained pit shapes on the contact surfaces [28]

19

Chapter 3

3. Materials and Methods

20

3.1. Tribological model From literature it is clear that gear is the critical component in the wind turbines. Based on the

accessibility to the surface for investigation and criticality of the component a tribological model is

adapted to validate the vision system for monitoring gear contacts. Twin-disc are frequently used as a

model to experimentally simulate gear contacts. Contact condition with combined rolling/sliding is

well defined from existing research. Figure 30 shows a typical twin disc contact which was used as a

base tribological model in our investigation. These two discs as shown in the Figure 29 can

experimentally simulate the contact at the pitch line of the gear contact where pure rolling motion

takes place. The setup can work in tree tribological regimes. The boundary, mixed and hydrodynamic

lubrication. Where in the boundary lubrication there is contact between the asperities whereby the

friction is at a high level. Then the hydrodynamic is the other extreme where there is no direct contact,

the two discs are divided by a small film of lubricant. In this regime the friction is minimal. Then there

is the mixed lubrication which is a combination of the other two. Due to the fact that only one of the

discs will be driven the other disc will suffer from slip.

Figure 30 Twin disc schematic representation [30]

3.1.1. Description of the experimental setup

The test setup that is used for the thesis is a modified FZG (Forschungsstelle für Zahnräder und

Getriebebau). The setup was earlier used to determine the extent to which gear lubricants help to

prevent scuffing on the tooth faces. The test setup-was already equipped with displacement sensor,

force sensor and temperature sensor. However, additional instrumentation were done to facilitate the

vision system for online monitoring.

The setup is built with a multi-sensor architecture:

1. Pyrometer

2. Two vibration sensors

3. Torque sensor

4. LVDT sensor (linear variable differential transformer)

5. Two hall-effect sensors

6. Vision system

A vision system is installed to check the surface change as a function of wear.

21

Figure 31 Test setup front view Figure 32 Schematic representation of the adjusted FZG test rig

On these figures the sensors and there position can be seen. On the schematic representation on figure

33 it is shown in more detail.

Figure 33 Schematic representation test setup

Self-aligning bearing

Load cell

LVDT

Loading

arm

Torque

sensor

Steel

disc

Steel

disc

Camera Pyro sensor

LVDT

22

The temperature sensor (pyro sensor) is used to measure the surface temperature of the driver discs

surface and can be found on the left on the block where a whole was made to fit it. It can also be seen

on the figure below. The vibration sensors are used to measure the vibrations that result from the

contact and damage of the discs.

The hall sensors are installed near to rotating shafts where the rotation of a steel block on the surface

then can be measure. One is used on the driven line and the other one on the driving line. With this

the slip of the driven disc can be calculated.

A new lubrication circuit was built in the existing twin-disc to experimentally simulate the gear contact

under lubricated environment. The lubrication circuit is implemented with bypass so that the flow can

be controlled. The lubricating circuit (see Figure 34) comprises oil reservoir, pump filter, drain and a

bypass for regulating the oil supply. The lubricating oil is supplied to an enclosed chamber where the

twin disc are loaded with specific normal force. The load is applied by means of dead weight. Pockets

were made to the existing chamber to install the vision system for real time monitoring. Multiple

interface was used where one screen is used for the vision program and the other one is setup for the

other sensors. Immediately after the contact the oil is transferred to the reservoir where the debris

are collected by means of a powerful magnet.

Figure 34 Oil circuit lubrication pump and the magnet arrangement for capturing the debris.

To separate the generated particle (from pitting) a magnet is placed in the oil outlet hole they are

attracted by the power super magnet.

Driver and driven discs. The Technical drawing for the specimen disc is shown in figure 35. The

diameter, contact width and curvature of the disc is selected based on the contact pressure calculation.

The centre distance between the driven and the driver is 165.5 mm. Both the driver and the driven

disc are machined with equal diameter. The diameter were chosen in order to avoid the variation in

the contact pressure due to the reduced radius.

23

Figure 35 Driven disc dimensions

Figure 36 Driving disc dimensions

3.1.2. Lubrication

Wind turbines are sophisticated machines, often operating in demanding environments. As such, it is

very important to select the right lubricant, for the proper oil choice can improve wind turbine

availability. Many performance dimensions must be considered in developing a well-balanced wind

turbine gear lubricant. Some of these include micropitting protection, gear wear protection, bearing

protection, corrosion inhibition, water tolerance, and shear. When used in a gearbox the lubricant

provides two primary benefits: to lubricate the teeth and to remove heat generated from the gear

operation. The lubricant is also often used for lubricating the various bearing found in the gearbox. If

the correct lubricant is selected for use in a gear system it will provide slip-free power transmission at

high mechanical efficiency, with good reliability, low maintenance, and long life [31]. Mobil supplies,

24

amongst other lubricants and greases, the synthetic gearbox oil Mobilgear SHC XMP 320, which is used

in more than 30,000 wind turbines across the world. For low speed gears at higher temperatures up

to 52°C ISO VG 150 - ISO VG 320 [32].

3.2. Selection of experimental condition for pitting detection The present test set-up operational parameters can be comparable with the existing reports by S.

Glodez, H. Winter and H.P. Stüwe at the University of Munich. They also developed a model for the

wear of gear flanks by pitting where they used hardened steel 42CrMo4 [28]. Using experimentally

determined material constants the number of stress cycles required for crack propagation through six

grains was then determined. The following table was found. The maximum contact pressure for the

gears is 1,406 GPa.

Table 3 Numerical results of the crack growth simulation [28]

Then to validate some experimental results are shown in table 4. The gears have been subjected to the

operational loading torque of 183.4 Nm and the number of revolutions of the pinion was 2175 rpm.

When pits of the size about 0.5 mm (see figure 28) have been observed on any tooth flank, the test

run was stopped and the corresponding number of loading cycles was recorded [28].

Table 4 Experimental results of FZG—pitting test [28]

Comparing the numerically estimated total number of stress cycles required for the occurrence of

pitting, N = 3.451 x 106, with the statistical representation of the experimentally determined number

of stress cycles (table 5), e.g. N = 2.629x106 to 3.765x106 stress cycles with 90% probability, one can

observe a reasonable agreement of results.

Table 5 Comparison of numerical and experimental results [28]

25

3.3. Material, contact pressure, speed, time

3.3.1. Material of gears in wind turbine

Case hardening is necessary to achieve the tooth strength and wear resistance in ferrous alloys and

the process allowing the most economical use of materials is carburizing. The gear is composed of high

strength steel alloy (42CrMo4) with a surface hardness of approximately 55 HRc. Its material

parameters are given in table 6.

Table 6 Material parameters of 42CrMo4 [33]

3.3.2. Contact pressure wind turbine

Depending upon their location within the gearbox, some of the bearings and gears need to carry large

loads at low speeds while others carry lower loads at much higher speeds. Since only one lubricant is

used throughout the gearbox, the lubricant film thicknesses will vary greatly from one location to

another. During periods when the turbines are in standstill, small amplitude vibrations can lead to

fretting wear of the gearbox components. Because of the variability of wind conditions, torque

reversals from generator engagement can happen thousands of times per year. Therefore during these

events, high-contact stresses are experienced by the gears, which can result in catastrophic tooth

fracture. The values of the following table where taken into account. This is for a 750 kW system.

Gear Element Teeth Mate

Teeth

Root Diameter

(mm)

Helix Angle Facewidth

(mm)

Speed

Ratio

Planet gear 39 99 372 7.5°L 227.5

Ring gear 99 39 1047 7.5°L 230 5.71

Sun pinion 21 39 186 7.5°R 220

Intermediate gear 82 23 678 14°R 170 3.57

Intermediate pinion 23 82 174 14°L 186

High-speed gear 88 22 440 14°L 110 4.00

HSS pinion 22 88 100 14°R 120

Overall speed ratio 81.49 Table 7 Gear Dimensions and Details [5]

For a 750 kW wind turbine generator with rotational speed of 1800 rpm the torque can be calculated

[5].

𝑃 = 𝑇 ∙ 𝜔 → 𝑇 = 𝑃

𝜔=

750 𝑘𝑊 ∙ 60

2 ∙ 𝜋 ∙ 1800 𝑟𝑝𝑚= 4 𝑘𝑁𝑚

If we than take a diameter of 10 cm for the first gear.

𝐹 =𝑇

𝑑=

4 𝑘𝑁𝑚

0,05 𝑚= 80 𝑘𝑁

26

The line force per length can then be calculated.

𝐹 =𝐹

𝑙=

80 𝑘𝑁

110 𝑚𝑚= 0,73 𝑘𝑁/𝑚𝑚

This value is reached at maximal power output.

Figure 37 Setup of wind turbine gearbox [5]

3.3.3. Design of experiments

An experimental design fulfilling various conditions were implemented for studying the limitations of

the newly developed vision monitoring system. The test matrix can be seen in Table 7. Tests were

performed for various load and speeds, additionally the curvature of the contact surface was changed

to accelerate the test condition. In the test matrix as given in table an exploratory methodology was

followed where the final aim is to see pitting on the contact surface with the minimal test duration.

Load (kg)

Speed (rpm)

Contact Lubricant Test Duration

Max Contact Pressure (MPa)

Cycles

Test 0 4 250 Line (4mm) Oil 8 days 342.8 2 880 000

Test 1 5 300 Line (6mm) Oil 66 hous 297.7 1 118 000

Test 2 8 410 point Oil 45 hours 1570 1 107 000

Test 3 8 410 point Oil 113 hours 2070 2 780 000

Test 4 20 300 /410 Line (6mm) Dry 95 hours 543.5 3 092 000

Table 8 Test conditions

In test 4 the first 55 hours (990000) the test ran with a speed of 300 rpm. To increase the cycle rate

the speed was increased to 410 rpm for the last 40 hours (984 000 cycles), thus a total of 1974000

cycles. Also the same sample was taken as test one to have the straight surface and more cycles. Which

makes for a total of 3092000 cycles.

27

Chapter 4

4. Remote Vision System

28

4.1. Remote Vision System (RVS) Visual inspection is always the validating variable for defect identification, though different kinds of

system are accommodated for performing the visual inspection most of these system uses an offline

methodology. To be able to detect different wear mechanisms an in-situ system has to be

implemented that can detect those defects. The vision system has two components 1. Camera and the

2. Lens. Today there are systems on the market for various applications which are faster, cheaper and

efficient. To find the best system for the wear mechanisms in different materials and different

machines a thorough study has to be done. A system that operates fast enough and can be

implemented with a microscope or a lens system is to be identified. Illumination also has to be

identified to get good quality images of the surface. In literature different systems can be found and a

few of them will be discussed here.

The first and most used system in wind turbines inspection is the visual inspection by service engineers

where personal subjective intervention is required. On the second place SEM and AFM analysis can be

performed from a section of the defective component. The goal of this this thesis is to do real-time

visual inspection from a remote location from disassembly. From the earlier research using SEM

examinations it is clear that scars and other surface damages form wear mechanism are in microscopic

scale [26]. A conclusion from naked eye observations is thus mostly for global wear characteristics at

macro scale. For different wear mechanisms and damages different vision techniques are used (See

figure 38). From these techniques SEM and OM are most used. SEM is an expensive technique and is

more used for research purposes. OM also has its limitations such as depth and field which can distort

the image.

Figure 38 Microscopic techniques used in tribological investigation [26]

With: OM = optical microscope, SEM = scanning electronic microscope, TEM = transmission electron

microscopy, SPM = scanning probe microscopy, FIB = focus ion beam, AFM = atomic force microscopy

and STM = scanning tunnelling microscopy.

29

Some different techniques were presented which includes line scan camera, laser triangulation and

area scan camera. However, all these techniques vary in terms of accuracy, characteristics, flexibility

and rigidity. This is also due to the fact the target application for measurements are different for these

versatile devices.

4.1.1. Line scan camera

A possible options for wear measurement is a line scan camera. This is a high speed system which can

acquire the surface characteristics. The camera only captures one image line in fast succession. For

two-dimensional image acquisition, motion is required in addition to the inspection: either the object

to be captured is moved or the camera is moved along the stationary object. With this technique a

continuous image of the surface damage can be made at very small scale [34].

Advantages:

Price/pixel: line scan offers much more cost-effective implementations of very high spatial

resolution image capture;

Dynamic range that can be much higher than alternative image capture methods;

High pixel fill-factor (typically 100%) to maximize sensitivity;

Blur-free images of fast moving objects;

Processing efficiency: line scanning eliminates the frame overlaps required to build a

seamless image. Frame overlaps represent redundant data that uses up precious processing

bandwidth, particularly in high-speed, high-resolution applications [5]

High speed system from 10 kHz to more than 100 kHz per line

Disadvantages

Needs specific lighting

Does not work for gears (too

low field of view)

Figure 39 Arrangement for line scan camera [34]

4.1.2. Laser triangulation

Triangulation means the measurement of distance by calculating the angle. In measurement

technology a sensor projects a laser spot onto the measurement object. The reflected light falls

incident onto a receiving element at a certain angle depending on the distance. The distance to the

target is calculated in the sensor by the position of the light spot on the receiver element and from the

distance of the transmitter to the receiving element [35]. This system is used for the inspection of train

tracks (condition monitoring system for the train track) as in figure 40. There are some relatively cheap

systems on the market but those are not fast enough for our purposes. This system is similar to the

line scan camera and work good for low relief surfaces, discs, rings. The image of the gears is sufficient

to analyse because of the fact that the teeth of the gear can be accommodated within the image. The

speed of the system is also similar to the line scan where one line at a time is taken. However, due to

the requirement of large coverage area this might consume significant time for acquisition.

30

Advantages:

Continuous image

Nice 3D image

Flat surface works fast for train tracks

Works even when an object is fully

immersed in a solution

Disadvantages

Requires larger installation space

Small surface relief of object in comparison

to camera object distance

Expensive

Needs more processing

Speed depends on max camera speed

Figure 40 laser triangulation system [36]

4.1.3. Machine vision with area camera

In comparisons to the line scan camera here every time an area picture is taken so there is not a

continuous image. Higher speeds can be achieved here because a significant coverage of the surface.

Advantages:

Simple system

High speed (10 – 200 fps)

Cheap

Disadvantages:

No continuous image

Needs a trigger

Needs appropriate lighting

4.2. Camera selection for the RVS A wide range of options are available for cameras, in the present work the EMVA standard was

followed for selecting the camera. The two most commonly available camera systems are CMOS

(complementary metal-oxide semiconductor) and CCD (charge-coupled device).

4.2.1. CMOS vs CCD

Both CCD and CMOS image sensors start at the same point, they have to convert light into electrons

and are both made out of silicon. But are two different technologies for capturing images digitally.

Each has unique strengths and weaknesses giving advantages in different applications.

In a CCD sensor, every pixel's charge is transferred through a very limited number of output nodes

(called vertical shift register) to be converted to voltage, buffered, and sent off-chip as an analog signal.

All of the pixel can be devoted to light capture, and the output's uniformity is high. The main pros for

the CCD sensor are the fact that it is a mature technology and the large photo sensor with high

sensitivity, signal to noise and dynamic range. The major cons are high power consumption, high

complexity driver circuit so you need a big camera for the heat, and it also has a high cost due to its

complexity [37-39].

31

In a CMOS sensor, each pixel has its own charge-to-voltage conversion. So there is no vertical shift

register, it is very simple. The sensor often also includes amplifiers, noise-correction, and digitization

circuits, so that the chip outputs digital bits. This is one of the reasons why the CMOS sensor can go

much faster. But due to this only a part is used for photo capture, nevertheless this has been improved

the last years. The biggest pros are the fact that it can run at high speed even at high resolution, it uses

less power than CCD, it has less vertical smear, and is cheaper. The biggest cons are lower sensitivity

and lower signal to noise [37-39].

Figure 41 CCD vs CMOS sensor [39]

CCD CMOS

Pixel access: only full image single pixel possible

Power: relatively high low power consumption

Integration: only sensor on chip more functions on chip possible

Conversion: after charge transfer (full image) at each pixel

Noise: FPN, statistical additional noise sources

Blooming: antiblooming techniques natural blooming immunity

Smear: caused by charge transfer no image smear possible

Table 9 Comparison of CCD and CMOS technology [40]

Another main difference is the description of the image speed. CCD sensors are described in terms of

images per second or frames per second, which is not useful with CMOS sensors if you consider the

single pixel or the ROI access. Here, we are talking about the pixel clock: for example, a pixel clock of 5

MHz will lead to about 65 frames per second if the image frame is 320 x 240 pixels [40].

4.2.2. Shutter Technology

There are two different technologies for the shutter. The rolling shutter or the global shutter. A rolling

shutter doesn’t capture the image in one time but rather by canning across the scene rapidly. This can

create distortions in fast-moving objects. This in contrast with a global shutter where the entire frame

is captured at once. It controls the incoming light to all photo sites simultaneously. It can be mechanical

or electronic [41].

Rolling shutter integration is not well-suited for capturing short-pulse light sources such as strobe light.

Unless the light source remains on for the duration of exposure, there is no guarantee that adjusting

shutter time or strobe duration will result in adequate capture of the light source. The row-by-row

process of light collection in a rolling shutter camera can also result in a switch noise effect, particularly

32

at high gain settings [42]. Some motion blur may become apparent. An example of an image taken

using a rolling shutter is below.

Figure 42 Motion blur with rolling shutter on the left, global frame shutter on the right [42]

4.3. Explanation ISO norm Choosing the suitable camera for a given machine vision application often proves to be a challenging

task. The EMVA1288 standard was put together by the European Machine Vision Association to

develop a unified and meaningful method of measuring a camera’s imaging performance. It is different

from consumer cameras which are often measured in lux [43]. Lux is a measurement of intensity as

perceived by the human eye. It is modelled using the response of the human eye and may not be

representative of how a machine would recognize an image. In addition, the lux value of a camera

represents the minimum illumination the camera requires to capture an acceptable image. Not only is

the definition of an “acceptable image” subjective, it doesn’t provide any information on image noise

[44]. Instead of lux, the EMVA1288 uses metrics such as quantum efficiency, read noise and full well

depth to describe a camera’s performance. Each measurement is characterized using a standardized

method defined by the EMVA1288 standard, providing an objective performance comparison between

different cameras from different vendors [44].

4.3.1. Quantum efficiency

Quantum efficiency (QE) is a measurement of the sensor’s ability to convert photons to electrons. This

can be seen on the figure below. A sensor with higher quantum efficiency is better for low light

applications due to better conversion efficiency. The quantum efficiency for a given sensor is

influenced by its photodiode design and will vary across the light spectrum [44]. If two sensors had the

same pixel size and saturation capacity, and sensor A has 20% higher QE at a particular wavelength

than sensor B, then sensor A is more sensitive and will require 20% less light to achieve the same image

intensity. This means less illumination is required to achieve the same result [44]. However, one has

to remember that having images to be acquired at a relatively high speed will require more

illumination.

33

Figure 43 Working of sensor [45]

4.3.2. Read Noise

Temporal dark noise (read noise) is noise generated by the sensor and camera circuitry and is

influenced by the electrical design. Temporal dark noise can be amplified when the camera gain is

increased, degrading image quality as a result. A low temporal dark noise allows for more signal gain

without sacrificing image quality. Historically, CCD sensors have much lower temporal dark noise when

compared to CMOS.

4.3.3. Saturation Capacity & Dynamic Range

Sensors with a larger light sensitive area (larger pixel size) will be exposed to more incoming photons

and generate more charge, leading to a higher saturation capacity. It means an analog to digital

converter will be able to convert the electrons in to more grey levels, resulting in a higher dynamic

range in the captured image [44]. This saturation capacity and pixel size can be seen on the figure

above. The dynamic range of a sensor is defined by the largest possible signal divided by the smallest

possible signal it can generate. The largest possible signal is directly proportional to the saturation

capacity of the pixel. The lowest signal is the noise level when the sensor is not exposed to any light,

also called the "noise floor". A high dynamic range camera will be able to produce enough detail in the

darker areas for the license plate to be recognized [43, 44].

4.3.4. Sensitivity

A common misconception is that a higher sensitivity camera should yield a brighter image for the same

exposure time when compared to a low sensitivity camera. This method of comparison ignores the

difference in saturation capacity and temporal dark noise between two cameras. To understand how

this works, the bucket analogy can be used to explain what happens when a pixel is exposed to

incoming light.

Figure 44 Bucket system [43]

34

Sensor pixels can be viewed as buckets catching rainfall (photons). A larger bucket will have a larger

volume (saturation capacity). The volume of rain collected will be proportional to the image intensity.

An empty bucket represents a black image while a full bucket represents a white image. If the rate of

rainfall is constant (constant exposure and illumination), a small bucket will fill up much quicker than

a big bucket, resulting in higher image intensity [43].

When evaluating camera sensitivity based on image brightness, the camera with smaller saturation

capacity will typically appear brighter when compared to a camera with larger saturation capacity. This

ignores the benefit that large saturation capacity brings, which is higher dynamic range. For

applications that are only detecting whether an object is present and do not require a high dynamic

range image, evaluating apparent sensitivity alone is a relevant way to compare. To improve apparent

sensitivity, cameras with low temporal dark noise are excellent choices, enabling the use of camera

gain to increase image brightness without sacrificing image quality [43].

4.3.5. Compering different sensors

With the ISO 1288 norm different sensors are compared with each other. These sensors are mounted

in some different cameras. Such as the flea3 series of gray point, the razer series of Basler and the uEye

cameras from IDS. In the table some different sensors are shown with a relatively big sensor size.

Sensor EV76C560 EV76C570 CMV2000 IMX174

Manufacturer e2v e2v CMOSIS Sony

Sensor Size 1/1.8" 1/1.8" 1/2" 1/1.2"

Pixel Size 5.3 µm 4.5 µm 5.5 µm 5.86 µm

Frame Rate 60 FPS 47 FPS 90 FPS 162 FPS

Read Noise 25 e- 20 e- 13 e- 7 e-

Saturation Capacity 8,384 e- 7,696 e- 13,500 e- 32,513 e-

Dynamic Range 50 dB 50 dB 60 dB 73 dB

Quantum Efficiency (525 nm) 59% 51% 53% 76% Table 10 Sensor specifications [44]

Normally with a bigger pixel size a lower quantum efficiency and a higher noise is found. But a higher

pixel size makes for a higher signal with a same light density. The signal has to be calculated with the

following formula: Signal = Light density x (Pixel Size)² x Quantum Efficiency.

Figure 45 Signal vs. light density chart for different sensors

0

5000

10000

15000

20000

25000

30000

100 300 500 700 900

Sign

al (

e-)

Light density (photons / µm²)

Signal vs. light density IMX 174

CMV2000

EV76C570

EV76C560

35

On this figure the IMX 174 always has the biggest signal of the 4 sensors. The saturation capacity is

also higher. The second best sensor is the CMV2000. To take into account the noise the understanding

figure is used. This is to make sure that you have a clear image with not a lot of noise. This can be seen

on Figure 46.

Figure 46 Signal vs noise

IMX174 will reach absolute sensitivity threshold at a lower light density the EV76C570 has the highest

threshold. Practically the different between these sensors is that the left is for the IMX 174 and the

right figures are for the sensor with a higher threshold. The bottom pictures are taken at higher speed

where the IMX 174 sensor is better and still able to have a good picture.

Figure 47 Comparing different sensor images [43]

4.4. Selection of lens The lens is the critical part of the vision system which gathers light from the object to improve

sensitivity and magnifies (or minifies) the field of view to match the size of the camera’s line sensor.

The working distance is also an important parameter, which is the distance from the cameras face

plate to the object being imaged.

It is very important that the sensor is aligned perpendicular to the direction of motion of the object.

The direction of the object with respect to the camera is also important and need to take into account

the flip and mirror effect of the lens to ensure that an object is moving through the field of view of the

camera that has the same direction as the sensor integration direction. The type of lens and working

distance needs special consideration to have the vertical resolution equal to the horizontal resolution

to obtain an accurate image [21].

0,00

10,00

20,00

30,00

40,00

50,00

60,00

0 0,2 0,4 0,6 0,8 1 1,2 1,4 1,6 1,8 2

Sign

al (

e-)

Light density (photons / µm²)

Signal vs light densityIMX 174 CMV2000

EV76C570 IMX 174 noise

CMV2000 noise EV76C570 noise

36

Figure 48 Areas illuminated by the lens and camera. The left is the appropriate choice [46]

Sensor sizes vary; they are categorized in size of 1/3”, ½”, 2/3” and 1”. The size is not a precise

dimension but determines that the sensor lies within a circle of the named diameter. For lenses a

sensor size is also specified. So the lens and the camera much match each other. Using a lens that

specified for a 1” sensor in combination with a 2/3” sensor is possible. Conversely this will not work

[46]. This because of the fact that the sensor will not fully be used. For the lens there are some

important parameters such as the magnification, the field of view and the working distance.

Working distance is the distance from the front of the lens to the object under inspection.

Depth of field (DOF) is the distance between the nearest and farthest objects in a scene that appear

acceptably sharp in an image. Although a lens can precisely focus at only one distance at a time, the

decrease in sharpness is gradual on each side of the focused distance, so that within the DOF, the

unsharpness is imperceptible under normal viewing conditions [46].

Angle of view is the extent of the observable world that is seen at any given moment. In case of optical

instruments or sensors it is a solid angle through which a detector is sensitive to light.

The field of view (FOV) is another way of representing the angle of view, but expressed as a

measurement of the subject area, rather than an angle [46].

For this thesis a telocentric lens is used. This because of the fact that this les has the unique property

of maintaining constant magnification over a specific range of object distances.

Figure 49 Working of telecentric lens [47]

Distortion is one of the worst problems limiting measurement accuracy. It’s defined as the percentage

difference between the distance of an image point from the image centre and the same distance as it

would be measured in a distortion-free image; it can be thought of as a deviation between the imaged

and the real dimensions of an object. For instance, if a point of an image is 198 pixels distant from the

centre, while a distance of 200 pixels would be expected in absence of distortion. High quality

telecentric lenses normally show a very low distortion degree, in the range of 0,1 % [47].

Telecentricity describes the angular deviation of the principal ray from a ray parallel to the optical axis.

A lower angle means that the lens will reproduce the image more accurately [47].

37

4.5. Illumination One of the most crucial aspects in machine vision is the selection of the correct lighting. Ambient light

is light that is reflected off of the part being inspected that comes from sources other than the lighting

components selected specifically for the machine vision application. It may be the overhead lights in

the plant, or the sun shining through a window or skylight. Ambient light is inconsistent by nature, and

the effects of ambient light on an application are usually dramatic and negative. If possible, the

dedicated illumination sources for the machine vision imaging must overcome, or be significantly

brighter than, the ambient light sources. Often, the ambient light is too strong or unpredictable, and

the inspection area must be shrouded to block the ambient light. It is almost never acceptable to use

ambient light alone for a machine vision application; the cost of the dedicated illumination and shroud,

where appropriate, will be repeatedly repaid in terms of consistency and reliability of the resulting

inspection [48]. In the past halogen light sources provided the best solution. However, LEDs have

evolved are nowadays even more reliable and more consistent for machine vision applications. For

machine vision it is important that there is adequate intensity, that you can control the intensity and

that the light is very uniform over the area of interest [49].

It is important to consider not only the brightness, but also its spectral content. Microscopy

applications, for example a monochrome LED source is useful for a CLOS camera, and also for color

applications.

Figure 50 Light Source Relative Intensity vs. Spectral Content [50]

In applications that requiring high light intensity, it may be useful to match the source’s spectral output

with the spectral sensitivity of your particular vision camera. Figure 51 shows that CMOS sensor based

cameras are more IR sensitive than CCD cameras, imparting a significant sensitivity advantage in light-

starved inspection settings when using IR LED or IR-rich Tungsten sources.

Figure 51 Camera Sensor Absolute Quantum Efficiency vs. Wavelength [50]

Human vision

38

The two graphs are compared with each other. Whit this there can be seen that for a CMOS camera

the biggest sensitivity is around 600 to 700 nm. For this solution a white led contains the proper

illumination frequencies. The bandwidth of the LED is also much bigger so more light can be detected

by the sensor. Because of this the LED will be able to detect more light at low lighting conditions.

The job then is to select the proper range of incident light emanation points that maximizes the

contrast for features of interest, while minimizing the contrast for non-critical features that may render

algorithms unreliable. The infinite range of possible solutions makes this appear to be a daunting task.

However, in practice the majority of applications can be reduced to one of three broad categories that

are commonly described as brightfield, darkfield and dome illumination structures [51].

Figure 52 Brightfield and Darkfield define the two [51] Figure 53 Dome or Cloudy Day Illumination [51]

Let us assume that the part under inspection is flat and has a reflectance value that is non-zero. The

incident lighting hemisphere can then easily be broken into two well-defined zones (see figure 52).

Since the angle of reflection equals the angle of incidence for specular reflection, all light emanating

from points within this angle will be reflected by the part back into the camera [51].

In bright field illumination the sensor captures most of the directly reflected light. Thereby, the lighting

direction is approximately perpendicular to the inspected surface. The surface appears bright, whereby

the features show as a continuum of grey levels. In dark field illumination the angle of the incident

light rays to the surface normal vector is very large. This results in a dark appearance of the surface,

but salient features, such as scratches, appear bright in the image. Selecting between bright field and

dark field illumination can enhance or hide surface qualities [52]. Effective application of dark field

lighting relies on the fact that much of the light incident on a mirrored surface that would otherwise

flood the scene as a hot spot glare, is reflected away from, rather than toward the camera. The

relatively small amount of light that is reflected back into the camera facilitates to acquire the edge of

small surface feature on the surface [50].

The Dome or Cloudy Day illumination approach is simply the combination of both brightfield and

darkfield lighting modes. The intent here is to provide light from all (or most all) points on the

hemisphere, which creates perfect shadow free illumination. The result is similar to the effect observed

during a perfectly overcast day, where the direction of the sun is completely indiscernible. Light

appears to come equally from all angles, and all objects are bathed in light equally from all directions

[51]. The following figure shows the different lighting solutions.

39

Figure 54 Different lighting solutions [53]

Preliminary test on illumination

Tests where done with the damaged pieces to select a suitable illumination system. An array of LEDs

were lined and images were acquired on a disc surface. It is clear from the image that the places with

no LEDs (in the array) looks much darker. This is typically to the spatial arrangement of the LEDs. The

illuminated areas appear to reveal more surface defects (pitting). A significant contrast can be seen

from the pitted area and the non-contact region. A contrast the vision program can detect the pitting

much better. This is due to the fact that the light that falls in the pits is not reflected back to the camera

but scattered around.

Figure 55 Pitting of test piece with direct illumination Figure 56 Ring light with pitting [51]

For les deep pitting and scratches dark field illumination is a better option. This can be seen on the

understanding image where the shallow pitting appears bright. Deeper pits will not be illuminated

properly with darkfield. Because of the shallow angle only the edges of the pit will be illuminated and

the bottom will be dark. This can be seen on figure 55.

Contact area

Non-contact

area

40

Figure 57 Shallow pitting with dark field illumination Figure 58 Darkfield illumination pitting [51]

A similar effect can be found in the literature where the top of the gear tooth is found with scratching

as seen on figure 59. Instead of pitting which can be found at the pitch line.

Figure 59 Scuffing of the top of the tooth and [23]

4.5.1. Strobe lighting vs continuous lighting

Both continuous lighting and strobing techniques can be used for machine vision applications. Strobe

illumination has evolved to include high-speed object inspection, often timed to an external event.

Continuous lighting

Continuous lighting or 100% duty cycle lighting is the most common form of vision illumination. With

the duty cycle defined as time on / total time x 100%. The primary advantage of continuous lighting is

ease of implementation and use, which generally also implies relatively low cost. Two significant

disadvantages of continuous-on vision lighting are lack of intensity in high-speed applications and heat

build-up. For high-speed applications camera exposure times must be shortened dramatically to freeze

motion to less than one pixel blur-the minimum necessary for an adequate inspection result. To

compensate, light intensity must be increased proportionally, but there is a practical limit to the

increase in intensity of continuous lighting [54].

Strobe lighting

An LED can be strobed for any duration that is within the safe limit of current as long as heat is

managed. The shorter the strobe time, the more power the LED can be over driven producing more

light intensity. LEDs can be switched on and off in less than 1 microsecond. It relies on the fact that

strobing at less than 1% duty cycle allows the light to process much more current, per unit time than

would be possible at 100% duty cycle, thus increasing light output. This strobe effect provides the

capability to capture sharp images of moving objects for machine vision applications [54, 55]. It also

41

provides a means to reduce or eliminate ambient light effects when the LEDs deliver greater energy in

a shorter exposure than the ambient light delivers to a camera with a shortened exposure time [56].

The light flash is typically coordinated with the camera exposure period in response to multiple,

external periodic events. Flash timing, frequency (repeat or flash rate) and duration (pulse width) are

determined by either the strobe controller’s internal circuitry, or externally via a trigger event

generated by a camera or part-in-place proximity signal [54, 56].

For LED lights, the effectiveness and amount of strobe over-current capacity are most closely related

to strobe duty cycle, LED and light design, thermal management strategy and drive electronics

capabilities [54].

As seen from the information above the dark field illuminations is the best fit to detect surface faults.

Another reason is the fact that the gear teeth of the wind turbine gears have a surface like finish and

would reflect the light to much with bright field illumination. Whereas dark field illumination only will

reflect the surface damage. To have enough photons falling on the sensor strobe lights can be used

which will have a relatively high intensity than normal continuous lighting.

4.6. Remote Vision System (RVS) The chosen parts were a 2/3” sensor size camera from IDS with the CMV2000 sensor, a telecentric lens

with a working distance of 65 mm and a magnification of 1x, a strobe controller and a LED light made

of High intensity LEDs. This camera was chosen because of the fact that the better but bigger sensor

(the IMX 174) is too big to fit on the cheaper lenses. For a bigger sensor the lens prizes are much higher.

The lens was chosen because of the prize, the working distance and the smallest pixel size possible

with still a good depth of field. This was achieved with the 1x magnification if bigger magnification

should have been chosen the DOF would have been too small. Now it is at a value of 1 mm. For the

illuminations direct lighting was chosen to see the pitting as dark and a strobe controller. More detailed

information for this choice can be found in the appendix C. The pixel size that is seen on the taken

images is then 5.5 µm per pixel.

42

Chapter 5

5. Image Acquisition And

Processing Using LabVIEW

43

LabVIEW V13 is the tool used to acquire and control the vision system. A program was specifically built

for implementing the vision system in the twin disc model. The program is connected to other sensors

and also facilitates to control rotation so that a trigger is made for acquiring the image at the same

location.

5.1. Theory behind vision detection

5.1.1. Image acquisition

The vision acquisition block within LabVIEW is used. The block has a standard framerate of 20 fps and

a resolution of 1920 by 1080p. The exposure time was set to manual and adjusted to get a good image.

Because of the bright LED lights the exposure time can be set very low and because of this the speed

while taking images could be relatively high.

Figure 60 vision acquisition block LabVIEW

From the vision block the finite acquisition with post processing is used. This acquisition is used for

acquiring a fixed number of images at once. The images will only be available to process when all

images are taken. This is very useful if the image processing time is much longer than the acquisition

time. Because the images are processed this option was chosen.

5.1.2. Different analysis systems

Texture analysis might be applied to various stages of the process. At the preprocessing stage, images

could be segmented into contiguous regions based on texture properties of each region. At the feature

extraction and the classification stages, texture features could provide cues for classifying patterns or

identifying objects.

Figure 61 The components of a typical computer vision system [57]

44

There are some different ways to analyse pitting and other surface damage:

First order histogram

Threshold with conditioning

Co-occurrence Matrix (Haralick 1979)

Fourier transform energy

Edge detection method

Local extrema count along a 1D scan direction (Mitchell 1977)

Directional intensity level energy (Hsiao 1989)

Mathematical morphology-derived features (Chen 1994, Lam 1997)

Statistical geometrical features (Y. Chen 1995: 16 features that describe geometry of binary

images obtained from texture by multithresholding).

From these methods three are chosen to use in the LabVIEW program. These are threshold value, The

Grey-scale co-occurrence matrix and the edge detection method. This because of the fact that these

functions were available in LabVIEW.

5.1.3. Threshold with conditioning

This is the most basic solution where a threshold value is given for the grayscale. This enables to

segregate the dark objects which corresponds to pitting. The images come from the first test and were

taken at the end of the test. The first image is the damaged surface taken with the IDS camera mounted

on the setup. Initially the image was converted into a grey image which was then used for the vision

processing. Furthermore threshold was used to classify the surface defects. This was the metric

threshold form LabVIEW for dark objects. An appropriate threshold value was chosen based on the

fact that the pitting was clearly visible with that specific value. Subsequently the image goes to a binary

state where the pixel is either red or black. Small objects where then filtered out because this may

appear as noise. Finally the holes are filled in to reduce the noise to calculate the whole area. This is

seen on the understanding flowchart and then subsequently on the images.

Figure 62 Flow chart vision based pitting detection

OriginalImage

Color Plane Extraction

Luminance

Treshold

Adv. Morphology

Remove small objects

Adv. Morphology

Fill holes

Original image

45

Figure 63 Pitting detection with LabVIEW

5.1.4. Grey-scale Co-occurrence Matrix

Grey level co-occurrence matrix has proven to be a powerful basis for use in texture classification.

Various textural parameters calculated from the grey level co-occurrence matrix help understand the

details about the overall image content. Texture is one of the important characteristics used in

identifying objects or regions of interest in an image. Texture contains important information about

the structural arrangement of surfaces. The textural features based on grey-tone spatial dependencies

have a general applicability in image classification[58, 59].

Haralick et al first introduced the use of co-occurrence probabilities using GLCM for extracting various

texture features. GLCM is also called as Grey level Dependency Matrix. It is defined as “A two

dimensional histogram of grey levels for a pair of pixels, which are separated by a fixed spatial

relationship”[58, 59]. Haralick proposed two steps for texture feature extraction: the first is computing

the GLCMs from a texture image I(x, y) and the second step is calculating texture features using the

calculated GLCMs [59]. The GLCM basically estimates the joint probability that a pair of pixel values

occur at a distance vector d from each other. Assume that visual texture I(x, y) is an N ×M matrix

consisting of G different grey shades, its GLCM for displacement vector d = (dx, dy) is an G × G matrix

[60, 61]:

Luminance

Threshold

Filter small objects

Fill holes

Original

46

Where δ (true) = 1 and δ (false) = 0. The number in the element (i, j) of the GLCM matrix, Pd (i, j), counts

the number of times that the pixel with value i occurred away from a pixel with value j [59]. An example

of this can be seen on the understanding figure.

Figure 64 An example image I(x, y) with 8 gray-levels is shown with its GLCM for displacement d = (0, 1) [60]

Haralick texture features are extracted using these GLCMs [59, 60]. To reduce the computational cost,

only five commonly used features are selected. These features are entropy, dissimilarity, contrast,

homogeneity, and Energy [60]. They are described in the following table [61]. By calculating this five

texture features it is possible to see how they behave for different textures [62].

Property Description Formula

Contrast Returns a measure of the intensity contrast between a

pixel and its neighbour over the whole image. ∑ (𝑖 − 𝑗)2𝑝(𝑖, 𝑗)

𝑖,𝑗

Dissimilarity Dissimilarity is a measure that defines the variation of

grey-level pairs in an image. It is the closest to Contrast

with a difference in the weight

∑ |𝑖 − 𝑗|2𝑝(𝑖, 𝑗)𝑖,𝑗

Energy Energy is a measure of local homogeneity and therefore it

represents the opposite of the Entropy. ∑ 𝑝(𝑖, 𝑗)2

𝑖,𝑗

Entropy Entropy in any system represents disorder, where in the

case of texture analysis is a measure of its spatial disorder ∑ 𝑝(𝑖, 𝑗)(− ln 𝑝(𝑖, 𝑗))

𝑖,𝑗

Homogeneity Returns a value that measures the closeness of the

distribution of elements in the GLCM to the GLCM

diagonal.

∑𝑝(𝑖, 𝑗)

1 + (𝑖 − 𝑗)2𝑖,𝑗

Table 11 Statistical GLCM features [60-62]

In the following paragraphs there will be done some test with the Haralick feature to see which of

them have the biggest potential to be used for the damage detection. For different images the Haralick

features are calculated and then shown in a graph to see which have the most potential.

47

5.1.4.1. Almost no surface damage

Figure 65 Almost no damage

5.1.4.2. Medium surface damage

Haralick Feature 1x1 2x2

Contrast 3,07 3.4

Homogeneity 0.81 0.72

Dissimilarity 0.54 1.19

Entropy 0.41 0.68

Energy 0.02 0.02

Haralick Feature 1x1 2x2

Contrast 3.43 3.84

Homogeneity 0.78 0.67

Dissimilarity 0.75 1.88

Entropy 0.51 0.89

Energy 0.02 0.01

Haralick Feature 1x1 2x2

Contrast 3.24 3.61

Homogeneity 0.78 0.68

Dissimilarity 0.73 1.76

Entropy 0.5 0.85

Energy 0.02 0.01

Haralick Feature 1x1 2x2

Contrast 3.22 3.6

Homogeneity 0.77 0.66

Dissimilarity 0.99 2.65

Entropy 0.56 0.99

Energy 0.02 0.02

Haralick Feature 1x1 2x2

Contrast 3.39 3.8

Homogeneity 0.77 0.66

Dissimilarity 0.92 2.46

Entropy 0.55 0.98

Energy 0.02 0.01

48

Figure 66 Medium surface damage

5.1.4.3. Major surface damage

Figure 67 Severe surface damage

5.1.4.4. Comparison

Figure 68 Compare damaged and non-damaged

Haralick Feature 1x1 2x2

Contrast 3.22 3.61

Homogeneity 0.78 0.67

Dissimilarity 0.92 2.44

Entropy 0.54 0.95

Energy 0.02 0.02

Haralick Feature 1x1 2x2

Contrast 3.72 4.09

Homogeneity 0.78 0.68

Dissimilarity 1.14 3.14

Entropy 0.57 1.01

Energy 0.03 0.02

Haralick Feature 1x1 2x2

Contrast 3.83 4.26

Homogeneity 0.76 0.64

Dissimilarity 0.914 2.4

Entropy 0.58 1.02

Energy 0.01 0.01

Haralick Feature Value pitting

Contrast 3.48 3.88

Homogeneity 0.78 0.68

Dissimilarity 0.92 2.52

Entropy 0.53 0.94

Energy 0.02 0.01

Haralick Feature Value good

Contrast 3.08 3.47

Homogeneity 0.81 0.7

Dissimilarity 0.63 1.62

Entropy 0.44 0.78

Energy 0.02 0.01

49

A quick comparison of the images of figure 67 to 69 is done for the Haralick features. The first thing

that is noticed is that the energy barely changes for both images. For the contrast a 12% increase was

found if an average of the vectors 1x1 and 2x2 is taken. For the homogeneity a small decrease of about

3.5% is found. The dissimilarity has an increase of 46% for 1x1 vector and a 55% increase for 2x2 vector.

The entropy has an increase of about 20%.

Figure 69 Graph of Haralick features. 1 = almost no damage, 2 = medium damage, 3 = severe damage

The graph is a representation of the images 67 to 69. Here a contrast increase of 16% was found, a

decrease of 3.5% for the homogeneity, a 62 % increase for the dissimilarity and a 24% increase for the

entropy.

5.1.5. Edge detection

5.1.5.1. LabVIEW solutions

Edge detection constitutes a crucial step in most of the computer vision applications. Sound edge

detection can provide valuable information for further image processing and interpretation tasks such

as image segmentation, object description etc. Edge detection techniques transform images to edge

images benefiting from the changes of grey tones in the images. Edges are the sign of lack of continuity,

and ending. As a result of this transformation, edge image is obtained without encountering any

changes in physical qualities of the main image [63].

There are different edge detection methods present within the LabVIEW platform:

- Laplacian

- Differential

- Prewitt

- Sobel

- Roberts

Analysis of a particular image using the above mentioned edge detection methods results in various

outputs (See figure 72). From this processing it is evident that Prewitt and Sobel offer the best

solution for edge detection.

0

1

2

3

4

5

1 2 3

Haralick FeaturesHomogeneity 1x1

contrast 2x2

Contrast 1x1

Homogeneity 2x2

Dissimilarity 1x1

Dissimilarity 2x2

Entropy 1x1

Entropy 2x2

50

Original laplacian Differential

Prewitt Sobel Roberts

Figure 70 Comparison between different edge detection techniques

5.1.5.2. Sobel

The Sobel operator performs a 2D spatial gradient measurement on an image and so emphasizes

regions of high spatial frequency that correspond to edges. Typically it is used to find the approximate

absolute gradient magnitude at each point in an input grayscale image. The operator consists of a pair

of 3x3 convolution kernels as shown in Figure 71. One kernel is simply the other rotated by 90° [63,

64].

Figure 71 Mask used by Sobel edge detection [64]

These kernels are designed to respond maximally to edges running vertically and horizontally relative

to the pixel grid, one kernel for each of the two perpendicular orientations. The kernels can be applied

separately to the input image, to produce separate measurements of the gradient component in each

orientation (call these Gx and Gy). These can then be combined together to find the absolute

magnitude of the gradient at each point and the orientation of that gradient [64].

5.1.5.3. Perwitt

The Prewitt edge detector is an appropriate way to estimate the magnitude and orientation of an edge.

Although differential gradient edge detection needs a rather time consuming calculation to estimate

the orientation from the magnitudes in the x and y-directions, the compass edge detection obtains the

orientation directly from the kernel with the maximum response. The Prewitt operator is limited to 8

possible orientations, however experience shows that most direct orientation estimates are not much

more accurate. This gradient based edge detector is estimated in the 3x3 neighbourhood for eight

directions. All the eight convolution masks are calculated. One convolution mask is then selected,

namely that with the largest module [63].

51

Figure 72 Perwitt edge detection mask [64]

5.1.5.4. Further processing

Ones the edge detection took place the further processing is done with a threshold value to go to a

binary image. Than just like setting a threshold value some other processes are used to get a nice

image. At the end of the processing a number of pixels are seen as pitting and this area is than saved.

5.2. Standalone LABVIEW program for twin-disc image acquisition and

processing

5.2.1. User interface

The interface exist out of 6 tabs to make it clearer for the user. One for the initialisation, than the

image, the particle date and then a graph and lastly the Haralick matrix with a graph tab.

On the first page (See figure 75) the folder is selected to store the saved data and images. Options for

number of images, saving location are displayed in the first page. Also indicators representing the

status of the machine and the vision system is clearly displayed.

Figure 73 Initialisation tab user interface

The second tab is the one who shows the images when they are being processed. After each trigger

the last acquired image will be visible in this interface.

Saving path image

DAQ input data indicators

Number of images taken

52

Figure 74 image tab user interface

The next one is the third tab than shows the date that was taken out of the processed image. This tab

is called particle data. This is the % area that consist out of pitting and the total area of pixels that is

pitting. This is stored in a table and in an excel file.

Figure 75 particle data tab user interface

The forth tab then shows the graph of the processed data from the previous tab. So you can see the

progress of the pitting.

Time stamps

Number of cycles

Number of vision particles

Data out of the vision block

Array with previous data

Total number of samples in a graph

Table with all the data

53

Figure 76 Graph tab user interface

The last 2 tabs show the processed information by using the co-occurrence matrix. It shows the

different Haralick features that were extracted out of the images. Five features are shown who were

chosen with some testing to see which has the biggest chance. These are than shown in a graph to see

the development of them.

Figure 77 Haralick tab user interface

Co-occurrence matrix

Haralick features

54

Figure 78 Haralick tab graphs user interface

The vision program is activated if the machine control program sets an output bit high on the DAQ.

This is done when the discs have slowed down and a pulse is given from the hall sensor. Then the

figures are taken, processed and stored. When starting the test this program first has to be initialised

to set the right speed, triggering condition and data logging. In the front panel of the program speed,

the number of rotations, temperature, slip and the running time are displayed.

Figure 79 Data acquisition and machine control program

55

5.2.2. Program The flowchart shows how the program is executed.

Figure 80 Flow chart Vision program

The flowchart below shows one possible process to determine the pitting on the surface. This is for every lighting and surface situation a different process.

The process below is the one that is used for the vision processing in the last test.

Figure 81 Flow chart Vision assistant processing

VisionAquisition

VisionAssistant

Particel data

Save Data

Buffer dataDisplay Buffer

Data

IMAQVision utilities

co-occurance matrix

Haralick functions

Save data

Buffer dataDisplay Buffer

Data

Save Image

OriginalImage

ImageMask

Color Plane

Extraction

Luminance

Tresshold

Adv. Morphology

Remove border objects

Adv. Morphology

Remove Large objects

Adv. Morphology

Remove small objects

Particle Filter

Remove long objects

Adv. Morphology

Fill holes

Particle analysis

56

In LabVIEW a block diagram was made to control the signals and processing. The first part are the

triggers to start taking images and to start saving the data. The red circle shows the data acquisition

block were the signals from the DAQ are read in. Two signals are used one from the hall sensor on the

driven side and a sensor that the disc slowed down. The blue circles are edge detectors so that the

true signal is only given in one rotation of the while loop. So that only one burst of figures are taken,

processed and stored. The yellow circle is a counter which is used to make sure that when the discs

slow down and multiples triggers from the hall sensor are given only one burst of images is taken. The

green circle than counts the number of rotations of the discs by using the trigger from the hall sensor.

Figure 82 Triggering in labVIEW block diagram

When the trigger is given the vision acquisition block takes the figures this is shown in 6.1.1. These

images are stored in an array to get processed. Because they are taken in one burst no delays take

place. The image blow shows the saving of the images. The file path is asked and the name for each

figure is generated. The images are then stored as PNG because of the fact that no information is lost.

Figure 83 LabVIEW saving of images

Each time an image is saved it is also processed by the vision assistant. Who looks for the pitting as

seen in the paragraph 6.1.3 and 6.1.5. The output of the vision assistant is a matrix with 2 columns one

for the % of pixels and one for the amount of pixels that is in pitting. This is for each pit who has its

size. These values are than added up. This is shown in the figure below. The red circle shows the vision

assistant. And the blue circles the blocks who separate the columns with the different values of the

output matrix.

57

Figure 84 Pitting detection processing

The get the image displayed it first had to be converted to another format and then back. This because

when he image is directly taken from the vision acquisition the image does not show. The image is then

resized to fit nicely in the frame.

Figure 85 Display of image

The image is beside the fact that it is checked for pitting also processed to look for the contrast,

entropy, … . The Haralick features that are taken out of the co-occurrence matrix. These are then also

used to look for surface damage. This is done with understanding program blocks. The shift registers

are used to add the date of each figure.

Figure 86 Haralick feature calculations Figure 87 Saving of data

All this data is than shown in a table and saved to an excel file. This is done with the code above. Al this data is then also shown in graphs to see the progress. The graph x axis shows the time. The time in the red circle is the time at the beginning of the test and the green circle the current time. The shift register is used to keep the time of the beginning of the test at the x scale minimum. The blue circle is a subVI called buffer were the data is stored to show in the graph. The history length is for the amount of samples to save and show.

58

Figure 88 Making of the graphs

The figure below shows the program inside the buffer subVI. Here each data point in stored in an array.

Figure 89 SubVI buffer in LabVIEW

59

Chapter 6

6. Test Results

60

6.1. Results dummy tests (test 0) This dummy test was performed to see if pitting can be observed for the selected condition which

represents the wind turbine gears. This test ran as long as possible in the timeframe of the thesis. Due

to the polishing the surface of one of the discs was convex and hence it carried an elliptical contact.

Because of this the contact surface was a lot smaller. So damage can occur much faster. From the 12

mm contact length only 3 mm is used. Which is a factor 4.

Figure 90 Contact surface D2 (Image size: 50 X 20 mm)

One of the disc has a diameter of 80 mm and the other one 87 mm. So if the hertz contact pressure is

calculated with a contact force of 20 kg (196N) (which is 4kg on the lever times 5) this makes for the

following result.

Table 12 Hertz contact pressure dummy test

The damage to the disc can be seen on figure 93. Where a full plot of the surface is shown. It is evident

that the damage is evenly spread along the whole surface. Pitting phenomenon can be seen as dark

phases and also the surface cracks perpendicular to the rolling direction is clearly seen. The size of the

pits ranges from 0.5 – 1mm.

Figure 91 Image taken with the new setup D2 (Image size: 6 X 10 mm). The driving disc is the D1 disc and the driven is

represented by D2. All the observations from the RVS are acquired on the driven disc D2 where the camera is installed.

The rotational direction of the images is from left to right. The images taken with the new camera setup all have a size of

6 mm by 11 mm.

PARAMETER 12 MM SYMBOL VALUE UNIT

Maximum hertzian contact pressure pmax 171.4 MPa Max shear stress τmax 51.5 MPa

PARAMETER 4 MM SYMBOL VALUE UNIT

Maximum hertzian contact pressure pmax 342.8 MPa Max shear stress τmax 102.9 MPa

Effective

contact surface

61

Here the full circumference of the disc can be seen. The images are aligned so the damage follows on

for every picture. These images are taken with the cheap microscopic camera. It is immediately clear

that the picture is much less clear.

Table 13 Damage at Surface

First image

pitting

62

Table 14 Damage at disc surface (D2)

The graphs (see figure 92) show the eccentricity of the discs measured by the displacement sensor.

Two sine waves mixed together can be attributed to the eccentricity of both the discs combined

together the second sine wave is disappears as a function of time which is due to the wearing of one

of the samples. There is no X axis because of the fact that these are two rotations that are taken of the

samples. On the surface there can clearly be seen that because of the eccentricity of the discs some

sectors have more damage than others. This is clearly seen on the third column of the first page were

severe pitting was evidenced.

pitting

63

Figure 92 Eccentricity measured by the LVDT sensor at the beginning, at 1 500 000 cycles and at 2 600 000 cycles

The roughness at the beginning had a Ra = 0.15 µm. After the test tis value was much higher but due

to the severe pitting the 3D measurement machine was not able to give a value. The can be seen on

the images below. These are then compared with the literature under need.

Figure 93 3D surface roughness after the dummy test (D2)

This can be compared with the literature of pitting in rolling contact fatigue. As a certain number of

alternating cycles is reached, a crack will nucleate from the inclusions or defects in this area, generally

at a depth of Zo, and become a source of other cracks. The cracks then propagate towards the surface

at an angle of 30° to the raceway surface and gradually become parallel to the surface. As the crack

reaches the work-hardened layer it extends in two ways. One is by penetrating the surface, the other

is by propagating underneath the work-hardened layer parallel to the race surface. The latter produces

laminar particles. When cracks first penetrate the surface, the lubricating oil may infiltrate into the

crevice. Pressure developed inside the cracks extends them towards the matrix of the area at a depth

of Zst. Some secondary cracks may initiate in the main crack and propagate from it under the action of

alternating shear stress, leading to the formation of fragments [65].

Figure 94 Jin and Kang’s rolling fatigue mechanism [65]

Rolling direction Rolling direction

30° slope 45°

64

6.2. First test complete setup This is the first test with the multi-sensor architecture where online images were taken during the test.

The camera acquired images every 20 minutes, the rotation during the image acquisition was slowed

down to 10 rpm. . A sample program from the database of IDS was initially used to acquire the images

with LabVIEW. Because of the size of the program the frame rate was merely limited to 1 fps. Another

reason for this was the fact that the program saves the images every time it takes an image which

slows down the program quite a bit due to the fact that every image had a size of about 5 Mbyte for

the 2048 by 1088 pixel size. This is bigger than the image resolution of the next test. Because of this

the image size taken also increased. This to a length of 11 mm instead of 10 mm. It takes therefor more

time to store the image before the next image can be taken. The images were triggered using a hall-

effect sensor. The images were almost acquired at the same place. The triggered image enables us to

compare the images efficiently with different stages of wear.

Figure 95 First image after trigger. Left: 55 000 cycles Right: 208 000 cycles (6 mm x 11 mm)

On the figures there can be seen that there is a slight difference but the rest of the picture is the same.

Other figures are better matched. Another problem is the fact that there is oil on the surface. This is

due to the fact that with slow turning discs the oil keeps to the surface. Of the trigger happens fast, an

oil free surface could be found. The following image could then be the result.

Figure 96 First image without oil on the surface taken after 256 000 cylcles of test 1

There were a lot of debris in the oil so the surface was contaminated when the images were taken.

There seems to be more damage on the surface of 97 than the one without oil. The image of figure 98

was taken approximately after 100 000 cycles. As can be seen on the figure there is not much damage.

Because of this the contact pressure for the next test will be increased. Here there was only a maximum

hertz contact pressure of 297 MPa with a mass of 5 kg which is a force of 200 N between the discs As

seen from literature this should be around 1000 MPa [66]. Another test had a maximum Hertz pressure

from 0.84 GPa to 1.88 GPa [67]. The diameter and weight of the samples was also taken at the

beginning and end of the test and here their also was no difference in the measurements.

65

Table 15 Contact pressure test 1

There is big difference between the mean Ra = 0.202 µm and mean Rt = 3.08 µm and Rz = 2.4 µm.

Because of this the contact pressure will be bigger at one location. This can also be seen on the below

figure. The difference in diameter and profile along the width of the cylinder clearly illustrates the

possibilities for difference in contact pressure. The profile can be seen with high Rp and Rv which

represent the maximum peak and minimum valley. In appendix F more information is given for the

roughness values.

Figure 97 Roughness measurements of surface driven disc and damage on top with test 1 (D2)

With the white light interferometry 3D images were also acquired. On the left figure the polishing with

the 800p sandpaper can clearly be seen. Some deeper grooves are because of the other sand papers

with a rougher finish.

Figure 98 3D Images of the surface in detail of test sample 1 (D2) Table 16 Roughness values test 1 (D2)

In the first test the oil gear pump had a max temperature of 37°C where it stabilized and the surface

of the discs stabilised at a temperature of 54 °C.

Parameter Symbol Value unit

Maximum Hertzian contact pressure pmax 297.7 MPa

Max shear stress τmax 89.4 MPa

Depth of max shear stress z 0.084 mm

Rectangular contact area width 2b 0.214 mm

Roughness parameters

Rp 1.01 µm

Rv 1.39 µm

Rz 2.40 µm

Rt 3.08 µm

Ra 0.202 µm

66

6.3. Second test In the second test the contact kinematics has been changed by introducing a curvature in the contact

surface. The LabVIEW vision was adapted so that the framerate was increased to 20 fps. Now this is

about 50 rpm to have some overlap. Because of the fact that the vision acquisition was used the picture

size was somewhat reduced to 1920 by 1080p. Here the image was post processed after the finite

images were taken. The calculations are done for a hertz elliptic contact. The normal force was

increased to 400 N and the speed was 410 rpm. The calculated contact stresses are close to the zone

of the found literature for having an earlier damage on the contact surface.

Effective Young's modulus E' 226.4 GPa

Effective contact radius R' 7.78 10-3 m

Semi contact width a 0.29 10-3 m

Semi contact width b 0.41 10-3 m

Mean contact pressure pm 1.04 GPa

Maximum contact pressure pmax 1.57 GPa

Table 17 Hertz contact stress [68]

Figure 99 Roughness profile before (D2) Table 18 Roughness measurements before (D2)

From the values in the table below a comparable Ra but a lower Rz and Rt can be seen. This is because

of the fact that the previous sample was polished under an angle. Here the contact pressure will be

much more equally distributed.

Figure 100 Roughness profile driving after test 2 (D1) Table 19 Roughness measurements after test2 (D1)

Pitting was not observed in the present test and also there is no significant roughness change in the

surface. This is due to the fact that local plastic deformation played a major role. Grooves from the

driven gear was also clearly observed in the driver (D1). In order to identify the lubrication region tests

Roughness parameters

Rp 0.6 µm

Rv 1.30 µm

Rz 1.90 µm

Rt 2.30 µm

Ra 0.219 µm

Roughness parameters

Rp 1.08 µm

Rv 1.33 µm

Rz 2.41 µm

Rt 3.42 µm

Ra 0.327 µm

67

were performed at variable speed with a ramp. As a result a Stribeck curve measured by starting at

300 rpm with boundary regime and passing through hydrodynamic regime at 1100 rpm was observed.

The green line is the mean value and the brown the medium taken of every sample set. Hence to

accelerate the condition the speed was increased from 300 rpm to 410 rpm where the coefficient of

friction is high.

Figure 101 Stribeck curve

The curves surface made for less oil on the surface when the pictures were taken. This makes the vision

detection easier. The oil supply was also turned down. This made for an increase in temperature of the

pump to 42 °C and the surface temperature to 65°C. The temperature rose very quickly in the beginning

and then stabilised around 64.5°C.

The better oil free images are like the following. Where surface features are used to see if the images

are taken at the same place.

Figure 102 Second test Left: first day 17u04 Right: second day 13u29 (6 mm x 10 mm)

The contact surface is oil free so that the images are clear. Both images are the 28 one taken in the

burst. There is some change in position but not that significant and some abrasion can be seen on the

surface. This is mostly due to the slip between the two discs. On this series of figures no pitting was

found. So after approximate 22 hours (400 000 cycles) no pitting was found. There was also no cracking

that would develop into pitting. The abrasion can also be seen nicely on the bigger images (see figure

103). This can also be seen on the LVDT graph where the value drops a bit in the beginning. For the

weight loss there was a drop of 0.01 gram for each sample.

68

Figure 103 Surface damage rings (13 mm x 40 mm) test 2. Left D2 and right D1

69

6.4. Third test For the third test the contact surface of the driven disc was machined. This with a diameter of 12 mm.

The same calculations were done like the previous test. The calculations are done for a hertz elliptic

contact.

Effective Young's modulus E' 226.4 GPa

Effective contact radius R' 4.65 10-3 m

Semi contact width a 0.2 10-3 m

Semi contact width b 0.45 10-3 m

Mean contact pressure pm 1.38 GPa

Maximum contact pressure pmax 2.07 GPa

Table 20 Hertz contact stress test 2-3 [68]

Here a high maximum contact pressure is found to promote an earlier damage. The contact pressure

was increased by a factor six when compared to the Test 1. The speed is also increased with reference

to the Stribeck curve taken at the previous test. Another difference beside the curved surface is the

fact that the surface is not polished. The cusp curves from the machining can be seen on the surface.

Both are the 26 image in the taken burst.

Figure 104 surface at beginning and after 21 hours (500 000 cycles) running in test 3 (D2)

High roughness values were found on the driven disc (D2) due to the fact that for this test the samples

were not polished. They were finished with the turning lathe to a shining surface. The machining marks

can be seen on the roughness measurement figures below. The Ra value is 0.36 µm which is rather

higher than 0.2 µm from the previous test that were polished.

Figure 105 3D images of the surface in detail before (D2) Table 21 Roughness measurements before (D2)

On the roughness profile the machining can also be seen. Here the 50 µm distance between the

different cuts is also visible. The calculated values of the roughness profile are then given in the tables.

Roughness parameters

Rp 0.9116 µm

Rv 1.1217 µm

Rz 2.129 µm

Rt 2.423 µm

Ra 0.3621 µm

70

Figure 106 3D images of the surface after test 3 (D2) Table 22 Roughness measurements after test 3 (D2)

After the test all the roughness values have increased because of the abrasive wear. The machined

surface is flattened out. Because of the rougher surface in the beginning actual contact surface is lower.

Therefore in the beginning the surface will wear much more. This can be seen from the abrasion on

the figures above. The co-occurrence matrix clearly points out the increase in abrasion scars as a unit

of time. This is done with the following images where the abrasion increase clearly can be seen.

Figure 107 Surface at the beginning, after 2 hours, 3.5 hours, 6 hours and after 20 hours and 48 hours

The figures are then processed with LabVIEW occurrence. The following can be found for the contrast,

homogeneity, correlation and dissimilarity.

Roughness parameters

Rp 1.01 µm

Rv 1.93 µm

Rz 2.93 µm

Rt 3.92 µm

Ra 0.502 µm

25 000 cycles

50 000 cycles

250 000 cycles

150 000 cycles

100 000 cycles

71

Figure 108 Graphs of Haralick features of figure 107

The features are selected based on the representativeness of the surface change which are contrast,

homogeneity, correlation and dissimilarity. Most of these features are increasing or decreasing at a

constant rate and at the end they level out because the severe abrasion occurs at the beginning. This

is clearly seen on the graph below where the beginning the correlation decreases and then stabilizes.

Figure 109 Graphs of real-time measurement of correlation

On the other hand the size of the contact area the diameter loss can be calculated. This comes for a 1

mm contact length and a diameter of 12 mm for the contact surface to a loss of 20.87 µm. This

diameter loss can be seen on the images below where the grooves is getting shallower.

4,5

5

5,5

6

1 2 3 4 5 6sample number

Contrast

0,38

0,4

0,42

0,44

0,46

0,48

1 2 3 4 5 6sample number

Homogenity

0,4

0,5

0,6

0,7

0,8

0,9

1 2 3 4 5 6

sample number

Correlation

5

10

15

20

25

1 2 3 4 5 6

sample number

Dissimilarity

0,88

0,885

0,89

0,895

0,9

0,905

0,91

0,915

0,92

0,925

0 20 40 60 80 100 120

Time (hours)

Image"Correlation"

72

Figure 110 Contact surface loss beginning and 20hours later (6 mm x 10 mm)

Seen from the LVDT sensor the diameter loss is around 12 µm. This is lesser than calculated because

of the fact that the material may also have undergone local plastic deformation.Due to the abrasion

groves are forming in on the contact surface. Because of the direct lighting the photons are reflected

away from the camera when they enter the grooves. Therefor a black line will appear on the image

can be seen on the graph below where the damage is rising.

Figure 111 Surface damage

The test was stopped after 2780000 cycles because of the fact that the surface suffered from “magic

wear rate” [69]. This is when pits and fatigue cracks form but are worn of due to the abrasion. This was

visible in the figure on top of this page. The image below was taken at the last logging interval of this

test. Where due to the surface damage the light is not reflected in the right way. In some sectors it is

too bright because it is reflected straight to the camera. At other places it is reflected away from the

camera. This makes it very hard for the vision program. Because of this the next test is done under dry

conditions, and bigger load to get the pitting faster. A flat surface is also chosen to get better lighting

conditions [69].

0

0,5

1

1,5

2

2,5

0 20 40 60 80 100

Time (hours)

Surface damage

73

6.5. Fourth test 6.5.1. Initial information

The fourth test exist out of different phases. For this test the oil circuit was disconnected and instead

the emulsion circuit was connected. The first run was a small run without emulsion, then the test ran

for 8 hours with emulsion and the last part was again in dry conditions. To increase the pitting the

lubricating part was taken out of the equation. The samples used are the same as with the first test.

Because damage did not yet take place and it has a flat surface.

In the previous test pitting did not occur there was crack initiation but no crack growth to pitting. This

is because of the fact that with the previous test magic wear rate took place and pitting did not occur.

Magic wear rate is the rate of wear at which any rolling contact fatigue cracks that are in initial stages

of development are removed either by natural or combination of natural and artificial wear. This is

influenced by the curvature, MWR increases with the increasing curvature and by the material

properties, MZR decreases with increasing hardness. The test pieces have a big curvature and low

hardness which makes for the MWR [69].

The first part in dry conditions was done to see if the temperature did not clime to high and to get

pitting much faster. The weight was also increased to 20 kg which comes to a force of 1kN on the discs.

Table 23 Contact pressure test 4

The contact pressure is almost double of the first test. After seeing that the dry condition did not give

fast pitting the emulsion was used to cool the discs. This was done because of the fact that overnight

the machine was running alone and nobody was checking the temperature.

6.5.2. Start of test There was one big problem with the emulsion and that is the fact that the lens of the camera got

fogged. This can clearly be seen on the following series of images.

Figure 112 Fogging of the lens after 1 hour in the beginning of test 4

Because of this, the amount of pixels to reach the sensor decreased a lot and the image became dark.

Another problem was the emulsion that stayed on the surface which made for bad images. The test

was than resettled and started again in dry conditions. The temperature was closely monitored to

make sure it did not climbed to high. The test chamber was not closed to led the head dissipate better.

This made for much better images as can be seen below. The lighting is also more equally spread.

Parameter Symbol Value unit

Maximum Hertzian contact pressure pmax 543.5 MPa

Max shear stress τmax 163.2 MPa

Depth of max shear stress z 0.153 mm

Rectangular contact area width 2b 0.39 mm

74

Figure 113 Better image quality with dry running at beginning (6 mm x 10 mm)

6.5.3. Test Images The figure above shows the image in the beginning. By taking the same image very burst a movie can

be made who shows the damage forming. This is also shown in the images below. The images are taken

approximately every 130 000 cycles. Every time the nineteenth image of the burst was taken to be

compared.

Figure 114 Damage formation at the same place on the discs (6 mm x 10 mm)

75

Most of the damage occurs at the other edges of the contact area. This will be due to uneven polishing.

The discs used in this test are the same as in the first test and there can be seen that the surface was

not completely straight. The contact pressure will be bigger at the outer edge. Another reason can be

because one of the discs has a smaller surface. On the figures above the formation of debris can be

seen. Here on the figures the formation of a long chip can be seen and at the end it breaks off. This

chip can then be found in the debris beneath the two discs. This can be seen on the figures below

where on the right a long chip can be seen. This picture was taken with the microscope.

Figure 115 Debris of the process

With the microscope pictures were then taken with a magnification of 400x. These are the outer

images below. Where he left one corresponds to the picture above and the right one to the middle

one. Here can be seen that the chip formation is not in one rotation but gradually.

Figure 116 Long chips under the microscope

Besides the longer chips also small debris is found. This can be seen below. Where the figures first have

these black markings and in the next data sample taken 20 min later at the same place these markings

have disappeared.

Figure 117 Formation of smaller chips

In this test there can also be seen that the original damage is grinded away.

76

Figure 118 Initial surface damage evolution. Beginning and end (6 mm x 10 mm)

Because of the dry conditions of the test corrosion took place on the surface. This can be seen on the

figures below. One during the test and the other ones after the test. The image during the test shows

it more clearly.

Figure 119 Surface corrosion during and after the test with the last 2 images

The image 19 of the taken burst where the progressions was showed above is then also compared with

the cleaned up image taken after the test. The left figure shows the last picture taken while the test

was running the right figure is the picture taken after cleaning of the sample with acetone. There can

than clearly be seen that there is pitting but much less than thought before. The black parts are debris

of steel that is coming loos from the surface.

Figure 120 Surface damage before and after cleaning of the sample (6 mm x 10 mm)

77

6.5.4. Program calculations results

6.5.4.1. Surface morphology

The graph under need shows the pitting of the surface by checking with a threshold value.

Figure 121 Graph of % area of pitting on the surface

The peaks in the beginning are because of the fact that the light was not reflected directly into the

camera. This can be seen on the images below. The images are taken at the same place but due to the

wear damage the right one appears darker. The following high values and lower ones are because of

this reason. The lower peaks found are when the light is again reflected straight into the camera. This

can be seen on the image at the low peak just after 10 hours (180 000 cycles). Between 32 and 55

hours the pitting percentage is also very low. This because the surface is smoothened out by the

abrasive wear as seen on the right image at 40 hours. The value at 40 hours (750 000 cycles) is not

equal to zero because of the fact that on other images of the burst pitting is found. This is seen below.

Figure 122 Surface damage on the surface (6 mm x 10 mm)

After 65 hours (1 400 000 cycles) the surface damage increases rapidly. This can also be seen on the

images where surface damage is increasing a lot.

0

0,2

0,4

0,6

0,8

1

1,2

1,4

1,6

1,8

2

0 10 20 30 40 50 60 70 80 90 100

Time (h)

Area % of pitting

78

Figure 123 Increased surface damage after 65 hours Figure 124 Declining of surface damage at the end

Because of the wearing of the damage and falling off of the debris the surface damage area is declining.

This is on the figure below but also on figure 114 where the debris fell off at the end.

6.5.4.2. Haralick features

Figure 125 correlations of the test

The graph of the correlation resembles the pitting graph. This is also like that with the homogeneity

but here it is not that visible. The Dissimilarity and contrast are approximately the same just with a

different value range. The contrast is seen on the right axis and the dissimilarity on the left one.

Figure 126 Dissimilarity and contrast Haralick features

The low peak in the beginning (3 hours or 50 000 cycles) is because of the adjustment of the exposure

time of the camera to a higher value. Because of this the image was much more illuminated. Just like

the on the image on the graph. The value then rises to a maximum as seen below and then goes down

0,87

0,875

0,88

0,885

0,89

0,895

0,9

0,905

0,91

0 10 20 30 40 50 60 70 80 90 100Time (hours)

Correlation

4,5

4,6

4,7

4,8

4,9

5

5,1

5,2

5,3

5,4

5,5

2,5

3

3,5

4

4,5

5

5,5

0 10 20 30 40 50 60 70 80 90 100

Time (h)

Haralick features

dissimilarity contrast

79

again. At the end (1 600 000 cycles) there is a very low value because of the fact that the surface is

smoother.

6.5.5. Other sensors For temperature some different peaks and changes are noticed. In the beginning the temperature rose

to a peak due to the heating of the friction. Then the machine was stopped to adjust the vision program

parameters to be able to detect the pitting. The temperature decreased when the machine was not

running. When it started again the temperature rose to 50 °C and stayed stable. After 55 hours

(990 000 cycles) the temperature rose to a temperature of 56 °C because of the speeding up of the

machine to 410 rpm. Another thing that is noticed is that the temperature in dry conditions is lower

than the 65 °C in lubricated conditions. From this there can be concluded that the oil has a bigger temp

due to the heat of the motor.

Figure 127 Surface temperature of test 4

The LVDT can be compared with the correlation between 0 and 20 hours and between 60 and the end.

In the beginning the first surface asperities are worn off, because of this the LVDT values decreases.

Then due to the abrasive wear the value then rises again. At 30 hours (540 000 cycles) the surface is

nicely smoothened. Then the formation of wear debris starts and the LVDT rises again. At the end due

to the formation and wearing off of the debris the value jumps quite a lot.

Figure 128 LVDT information of test 4

30

35

40

45

50

55

60

0 10 20 30 40 50 60 70 80 90 100

°C

Time in hours

Temperature

0

5

10

15

20

25

0 10 20 30 40 50 60 70 80 90 100

µm

Time in hours

LVDT

80

The friction graph is also measured, in the beginning the friction rises due to the wearing off of the

asperities. Just after 55 hours (990 000 cycles) the friction rises due to the speeding op of the machine.

The friction than decreases by the end due to wearing off of the debris as seen in figure 114. At 25

hours (450 000) there is a small peak. This can be compared with the correlation of figure 125.

Figure 129 Friction information of test 4

6.5.5.1. Measurements

Weight reduction

Each value was measured 5 times and the mean was taken to be compared. A weight reduction is seen

for both discs. This can also be seen in figure 115 where debris can be seen.

Driver (gram) Driven (gram)

Before 752.01 694.14

After 751.77 694.02

Loss 0.28 0.1 Table 24 Weight reduction in the fourth test

Wear rate

For each roller, the mean wear rate during the test was determined in terms of removed thickness for cycle, calculated as:

𝑊𝑅 = 𝑚

𝐷ℎ𝑁

Where WR is the mean wear rate, Δm is the mass variation from the beginning to the end of the test, r is the material density, d is the outer roller diameter, h is the specimen thickness and N is the cycles number.

Driver (D1) Driven (D2)

Diameter D 82.45 82.40 mm

height h 6 6 mm

Mass loss m 0.28 0.1 g

Density 7830 10-6 7830 10-6 g/mm³

Cycles N 1 974 000 1 974 000 cycle

Wear rate WR 1.16 10-8 4.16 10-9 mm/cycle Table 25 Wear rate calculations

-1,38

-1,36

-1,34

-1,32

-1,3

-1,28

-1,26

-1,24

-1,22

-1,2

-1,18

0 10 20 30 40 50 60 70 80 90 100°C

Time in hours

Friction

81

Surface roughness

The initial roughness measurements are taken from before test one. This because the same sample is

used. The first thing that can be noticed is that the Ra value rose from 0.202 µm to 0.921 µm. This

because of the damage on the surface. The Rt value also rose from 3.08 to 9.85. The damage can also

clearly be seen on the 3D images of the driven disc.

Figure 130 3D Images of the surface in detail at the beginning of D2 Table 26 Roughness values beginning of D2

On the damage surface of figure 131 the abrasive wear can clearly be seen as long groves on the

surface. The Z scale is also quite a bit higher than at the beginning with 17 µm against 7 µm. Other

parts of the surface are smoothened out.

Figure 131 3D Images of the surface in detail after the test of D2 Table 27 Roughness values after test of D2

For the roughness values of the driving disc the following is found. The biggest valleys and peaks are

increased quite a lot. This can be seen on the Rp and Rv values.

Table 28 Roughness values beginning of D1 Table 29 Roughness values after ofD1

Here a bigger difference is noticed. This is also seen on the 3D image. On this disc there are not as

much long abrasive wear scars but small pitting can be seen where small debris fell off.

Roughness parameters

Rp 1.01 µm

Rv 1.39 µm

Rz 2.40 µm

Rt 3.08 µm

Ra 0.202 µm

Roughness parameters

Rp 3.09 µm

Rv 2.66 µm

Rz 5.75 µm

Rt 9.85 µm

Ra 0.921 µm

Roughness parameters

Rp 3.83 µm

Rv 4.07 µm

Rz 7.90 µm

Rt 10.4 µm

Ra 1.30 µm

Roughness parameters

Rp 0.354 µm

Rv 0.526 µm

Rz 0.880 µm

Rt 1.38 µm

Ra 0.147 µm

82

Figure 132 3D image surface roughness driving disc (D1) after test 4

83

Chapter 7

7. Conclusion and

Future Work

84

7.1. Conclusion In the present thesis a new remote vision system (RVS) system was developed. Careful selection of the

modules in the RVS are made using the EMVA standards. The image acquisition and image processing

modules are well defined. The image acquisition module (CMOS sensor and the telecentric lens)

selected for the monitoring purpose stands effective from the view point of quantum efficiency and

coverage area respectively. A standalone image processing program was developed to acquire and

thereby process the images for understanding the surface defects.

With the newly developed RVS the evolution of surface defects and the dynamic behaviour wear

mechanism. Also it is evident the new RVS can operate under dry, oil and emulsion sprayed

atmosphere. Existing image processing algorithms present in Labview and newly developed Vis

particularly for particle analysis clearly displayed the wear trend. The exploratory methodology in

accelerating the test condition stood effective in achieving the required damage which was detected

in the RVS. On the whole the newly developed RVS stands effective and can be further used for damage

studied.

7.2. Future work This thesis is just the basis for a more detailed research thesis. To be able to have a fully working real

time vision system for wear monitoring on a wind turbine gearbox more research need to be done.

The first part is to test the system on a real gear instead of twin disc. Where with the gears the

triggering needs to be done just right to have clear in focus images of the gear surface. The program

needs to be optimised to give notifications when damage is getting to severe or if something else is

wrong. If the whole system is working automatically and in real time it needs to be mounted in a wind

turbine. This will be a big challenge because of all the different parameter that can influence the

system. The first problem will be the installation space, where to put it, and another challenge is going

to be the lubrication system which can have an influence in the figures as seen in this thesis. The system

than has to be connected to the internet to be able to give information to the engineers in the control

room. To have this system completely faultless a lot of research needs to be done.

85

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52. Pernkopf, F. and P. O'Leary, Image acquisition techniques for automatic visual inspection of metallic surfaces. NDT & E International, 2003. 36(8): p. 609-617.

53. dalsa, T., how to select the right camera for machine vision applications, F. serra, Editor. 2015. 54. Merva, D.M.a.J., Strobe Lighting for Machine Vision, in Quality magazine. 2010. 55. Pinter, M. TECH NOTE: Strobe; a new era for LED’s and Machine Vision Lighting. 2013. 56. Axelrod, N., LED: Smart Lighting for Machine Vision, in Quality magazine. 2010. 57. Zhou, D. Texture Analysis and its Applications. 2006; Available from:

https://www.cs.auckland.ac.nz/~georgy/research/texture/thesis-html/node7.html. 58. GADKARI, D., IMAGE QUALITY ANALYSIS USING GLCM, in College of Arts and Sciences. 2004,

University of Central Florida. 59. Koen Bertels, A.S., Parallel implementation of Gray Level Co-occurrence Matrices and Haralick

texture features on cell architecture. Springer Science+Business Media, 2011. 60. Sina Jahanbin, A.C.B., Eduardo Perez, Dinesh Nair, Automatic Inspection of Textured Surfaces by

Support Vector Machines. National Instruments, 2009. 61. Berchtold, J., Terrain appearance: Visual characterization using the right features, in

Autonomous Systems Lab. 2009, Institute of Technology Zurich: interweb. 62. A. Gebejes, R.H., Texture Characterization based on Grey-Level Co-occurrence Matrix, in

Conference of Informatics and Management Sciences. 2013. 63. Rajesh, N.S.a.R., Edge Detection Techniques for Image Segmentation – A Survey of Soft

Computing Approaches. Int. J. of Recent Trends in Engineering and Technology, 2009. 1(2). 64. Raman Maini, D.H.A., Study and Comparison of Various Image Edge Detection Techniques.

International Journal of Image Processing, 2004. 3(1). 65. J.E. Fernandez Rico, A.H.B., D. Garcia Cuervo, Rolling contact fatigue in lubricated contacts.

Tribology International, 2002. 36: p. 35-40. 66. Karel PETR, V.D., OPTIMIZATION OF GEAR-MESH SINGLE-STAGE GEARBOX FOR REGIONAL

TRAINS BY FEM. ZESZYTY NAUKOWE POLITECHNIKI ŚLĄSKIEJ, 2014. 67. Oliveira, T., EFFICIENCY TESTS OF A TRANSFER GEARBOX: BIODEGRADABLE NON-TOXIC ESTER

VS. MINERAL OIL. Mecânica Experimental, 2006. 68. abc, T. Fretting corrosion. 2013 [cited 2014; Available from: http://www.tribology-

abc.com/poll/previous.htm. 69. Kalousek, J., Wear, Plastic Flow and Rolling Contact Fatigue Damage in Heavy Haul, in Heavy

haul seminar. 2014. 70. IDS. UI-3360CP. 2015 [cited 2015; Available from: https://en.ids-

imaging.com/store/produkte/kameras/ui-3360cp.html. 71. Rui, W. and L. Gang, Trend Prediction of Oil Temperature for Wind Turbine Gearbox Based on

Grey Theory, in Emerging Research in Artificial Intelligence and Computational Intelligence, H. Deng, et al., Editors. 2011, Springer Berlin Heidelberg. p. 280-285.

72. Contrastech, Telecentric Lens, Contrastech, Editor. 2015. 73. Contrastech, Light catalog, Contrastech, Editor. 2015. 74. Budynas.R. HERTZIAN CONTACT STRESS CALCULATOR. 2014 [cited 2015 26/04/15]; Available

from: http://www.amesweb.info/HertzianContact/HertzianContact.aspx. 75. Nisbett, B., Tutorial 3-19: Hertz Contact Stresses, in Shigley's Mechanical Engineering Design.

2003. 76. Beek, A.v., advanced engineering design. 2012. 77. wisetool. Surface roughness and finished. 2013 [cited 2015 21/05/2015]; Available from:

http://www.wisetool.com/surfacefinish.htm.

88

Chapter 8

8. Appendix

89

8.1. Appendix A: Test protocol The procedure for traditional tribological test is developed based on preliminary tests done by Jacob Sukumaran. The procedure consists of four steps 1. Sample preparation 2. Reference measurements before wear testing, 3. Tribo testing and 4. Post-mortem analysis

TTS Step 1: Sample preparation:

TTS Step 1.1: Machining of steel disc using turning lathe (markings were made on the flat face to locate the same region for roughness measurement).

TTS Step 1.2: Polishing of steel disc with SiC abrasive papers (in the following order P80D, P240, P320 and P800)

TTS Step 1.3: The steel discs are cleaned by wiping the surface. Anti-corrosion spray is put on the surface to make sure there is no corrosion. Later with acetone the steel discs are cleaned.

Step 2: Reference measurement before wear:

TTS Step 2.1: Immediately after cleaning with acetone, the reference mass (m1) of the steel discs are measured for five consecutive times and its average is considered as mass loss.

TTS Step 2.2: The roughness and diameter of the specimen are measured at the marked locations. The roughness parameters measured for comparison purpose. The Ra value is chosen at 0,2 µm. as seen from references.

TTS Step 3: Wear testing:

TTS Step 3.1: Mounting of the steel discs.

TTS Step 3.2: The test rig was warmed up for a period of min 30 minutes before each testing.

TTS Step 3.4: The LabVIEW program is started and the parameters are selected. These are the speed, logging intervals and places where to save the data.

TTS Step 4: Post-mortem analysis

TTS Step 4.1: The two discs are cleaned with acetone to prepare for the measurements.

TTS Step 4.2:

The diameter loss (ΔD) is calculated from (D1b -. D1a).

The reduction in height of the V-groove (ΔVG) is calculated from (VG1-VG2).

TTS Step 4.3: The data from the LabVIEW program is than checked.

90

8.2. Appendix B: Vision system specifications 8.2.1. Camera

The Sony IMX174 CMOS sensor is the best sensor. But due to the fact that lenses for such a sensor,

who is bigger than normal, are not very common and more expensive the CMV2000 sensor was chosen.

This sensor is built by CMOSIS. With the sensor selected a camera needs to be found. The camera is

just the system who is built around the sensor and can come from different companies.

8.2.1.1. Camera

Here in Gent there is a firm called Phaer who sells camera’s. By looking through there camera options

with the CMV 2000 sensor the following was found. It’s from the manufacturer IDS. There was also

choses to use USB3.0 as interface to connect to the computer. The chosen camera is the UI-3360CP.

Figure 133 UI-3360CP vision camera from IDS with CMV2000 sensor [70].

Interface USB 3

Lens Mount C-Mount

I/O In I 1 x optodecoupled

Dimensions H/W/L 29.0 mm x 29.0 mm x 29.0 mm

Mass 44 g 44 g

Power supply USB Cable USB Cable

IP code IP30

Table 30 Camera information [70]

8.2.1.2. Sensor

All the specifications of the camera come from the sensor.

Sensor Technology CMOS Color

Manufacturer CMOSIS

Resolution (MPix) 2.23

Resolution (h x v) 2048 x 1088

ADC 12 bit

Pixel Class 2 MP

Sensor Size 2/3"

Shutter Global shutter

max. fps in Freerun Mode 152.0

Sensor Model CMV2000-3E5C1PP

Pixel size 5.5 μm

Optical Size 11.264 mm x 5.984 mm

Optical sensor diagonal 12.75 mm (1/1.25")

Table 31 Sensor information [70]

91

8.2.1.3. Temperature

The camera has an operating temperature of 0 to 50 °C. For the oil temperature of the gearbox the

following is found.

Figure 134 Data of gearbox oil temperature [71]

So to make sure the camera doesn’t overheat it has to be mounted on a place where the temperature

can be lower than the oil temperature of the gearbox. This should not be a big problem because of the

fact that the camera is mounted on the top of the lens and so will be at least 190 mm away from the

tested gear tooth.

8.2.1.4. Speed of camera

For camera has a frame rate of 152 frames per second. Which is 9120 ppm. For the fastest moving

parts of the wind turbine which is the high speed pinion which has a speed of 1800 rpm and 22 tooth

it comes to 39600 ppm. This is too high for the camera so if this system needs to be checked it cannot

be done at full speed. Even the intermediate speed gears cannot be checked at full speed where 10350

ppm needs to be checked to check all the tooth at one rotation. This can be helped to check only a

couple tooth per rotation.

8.2.2. Lens

The biggest issue for the lens was the cost prize. Therefor a lens from a Chinese company was ordered.

The company is called Contrastech. It has a magnification of 1 so will project the exactly to the sensor.

The other focusing points of the lens where the smallest pixel size of the image and the depth of field.

Table 32 Properties telecentric lens [72]

Figure 135 Telecentric lens photo and dimensions [72]

CLW-MP-1X-65

Magnification 1X

Working distance (mm) 65 mm

Telecentricity (degree) 0.08

Distortion 0.04

Depth of field (mm) 1.2

Mount C-mount

Field of view 8.8 x 6.6

92

The dimensions of the telocentric lens are a good fit for the test setup because of the fact that it fits

easily in the whole to install the camera with has a size of 40 X 40 mm.

8.2.3. Lighting

8.2.3.1. LED light

The lighting was made by myself instead of ordering a quite expensive ring led light. This was done

with high bright LEDs. The dimensions had to be so that the lens could fit through it and it had to fit in

the test setup which was 50 mm wide. The lens has a diameter of 30 mm. De LEDs are placed in the

holes who are 3 mm apart.

Figure 136 LED illumination panel

For each resistor 2 LEDs are used. This is due to the fact that the resistor has a maximum power of ¼W

to 1W. Above 1W they are called power resistors. Because of this not too much LEDs can be used per

resistor. The LEDs that are used are high bright LEDs with a Voltage drop across it of 3V. The source

that was used has a voltage of 24V. To make sure that it works properly some calculations need to be

done. The circuit exists out of 24V the source, a resistor and two LEDs.

First the voltage over the resistor is calculated. The 3 V is subtracted twice because of the fact that the

two LEDs are installed in series.

𝑉𝑅 = 𝑉𝑠𝑜𝑢𝑟𝑐𝑒 − 𝑉𝐿𝐸𝐷 = 24 𝑉 − 3 𝑉 − 3 𝑉 = 18 𝑉

Than to have about 20 mA per LED the resister is calculated.

𝑅 =𝑉𝑅

𝐼𝑅=

18 𝑉

20 𝑚𝐴= 900 𝛺

The closest is 960 𝛺.

𝐼 =𝑉𝑅

𝑅=

18 𝑉

960 𝛺 = 18.75 𝑚𝐴

93

Figure 137 LED light circuit

The thing which is mostly ignored is the fact that the resister has a maximum power.

𝑃𝑅 =𝑉𝑅

2

𝑅=

182

960 𝛺= 0.33 𝑊𝑎𝑡𝑡

This is a bit more than the ¼ Watt where the resisters are designed for. Because of this strobing of the

LED makes sure that there is no overheating of the resistors.

Figure 138 LED light

A cap was than drawn and 3D printed to fit on the LED light. This was printed by materialise for a minor

12 euro.

8.2.3.2. Strobe control

To have a higher light intensity at the time a picture is taken a strobe controller is used. The following

model was ordered: PLU-2460-4

Table 33 Properties strobe controller [73] Figure 139 Strobe controller [73]

Input Voltage AC 100-240V

Inrush Current 17A(AC100V),35A(AC240V) From a cold start

Output D C48V 5A MAX

Output Power Total for 4 channels: 60W MAX

Light Time 10-990 µs

Light intensity control light intensity can be set from 10% to 100%

Light Delay 10µs MAX

Trigger Function Yes

Trigger Input 5-24V

94

For the first test the strobe controller was not yet connected and a transistor circuit was made the

control the LED light with the DAQ controller from LabVIEW. The transistor BD650 has a base source

of 5V and an emitter source of up to 100 V so the 24V supply for the LED light is not a problem.

Figure 140 transistor circuit

95

8.3. Appendix C: Sensor

96

97

8.4. Appendix D: Lens

98

8.5. Appendix E: Contact pressure calculations The contact pressure calculation in the present work is calculated based on Hertzian contact formula.

The Hertzian equation for two different condition considered for the test matrix 1. Line contact and 2.

Elliptical contact are given below.

8.5.1. Cylinder on cylinder The two discs have a cylinder on cylinder contact as seen on the figure below. Consider two solid elastic

cylinders held in contact by forces F uniformly distributed along the cylinder length l.

Figure 141 Cylinder on cylinder contact [74] Figure 142 Contact stress across contact zone of width 2b [75]

The resulting pressure causes the line of contact to become a rectangular contact zone of half width b

given as [75]:

The maximum contact pressure pmax between the cylinders acts along a longitudinal line at the center

of the rectangular contact area, and is computed as [75]:

Calculation of Principal Stresses and Maximum Shear Stress [75]:

99

The maximum shear stress is thus given as [75]:

Table 34 Hertz calculation results [74]

When these equations are plotted as a function of maximum contact pressure up to a distance of

0.25mm below the surface contact point the following can be found:

Figure 143 Maximum shear stress [74]

Parameter Symbol Value unit

Maximum Hertzian contact pressure pmax 295,5 MPa

Max shear stress τmax 88,7 MPa

Depth of max shear stress z 0,085 mm

Rectangular contact area width 2b 0,215 mm

100

8.5.2. Elliptic contact For the latest test the surface was machined to a circular shape. This to increase the contact pressure.

The following formulas are form the book Advanced engineering design by Anton van Beek [76].

Figure 144 Elliptic contact [68]

For the elliptical contact surface the semi-axes a and b are related by

𝑎 = 𝑎∗ (3 𝐹 𝑅′

𝐸′)

1/3

, 𝑏 = 𝑏∗ (3 𝐹 𝑅′

𝐸′)

1/3

Where

𝑎∗ = 𝜅 [1 + 2(1 − 𝜅2)

𝜋 𝜅2− 0.25ln (𝜅)]

1/3

, 𝑏∗ = 𝑎∗

𝜅

1

𝜅= 1 + (

𝑙𝑛 (16λ

)

2 λ)

1/2

− (𝑙𝑛(4))12 + 0.16 𝑙𝑛(λ)

1

𝑅′=

1

𝑅′𝑥+

1

𝑅′𝑦,

1

𝑅′𝑥=

1

𝑟1,𝑥+

1

𝑟2,𝑥,

1

𝑅′𝑦=

1

𝑟1,𝑦+

1

𝑟2,𝑦

And λ the ratio of the effective radii R’x/R’y or R’y/R’x, the smallest value of these quotients. The

contact formulae of a circular contact may be derived from the formulae of an elliptical contact by

substitution of λ=1 and a=b=r.

The mean and maximum contact pressure of an elliptical contact are relate by

𝑝𝑚 =𝐹

𝜋 𝑎 𝑏′, 𝑝𝑚𝑎𝑥 = 1.5 𝑝𝑚

To calculate the maximum Hertzian contact load the semi-axes a and b are replaced by the non-

dimensional semi-axes a* and b*.

𝑝𝑚 =𝐹1/3

𝜋 𝑎∗ 𝑏∗ (𝐸′

3 𝑅′)

23

Substitution of the critical value of the mean contact pressure pm.c in pm gives

𝐹𝑐 = (𝜋 𝑎∗ 𝑏∗ 𝑝𝑚.𝑐)3 (3 𝑅′

𝐸′)

2

101

8.6. Appendix F: Surface roughness Ra = The average variation from mean line.

Rt = Distance from the highest peak to the deepest valley.

Rz = The average Rt over a given length.

Rz = (Rp1+Rp2+Rp3)+(Rv1+Rv2+Rv3) / 3

Rp = The highest peak above the mean line.

Rv = The deepest valley below the mean line

Table 35 Surface roughness measurements [77]