GUIDED WAVE STRUCTURAL HEALTH MONITORING FOR LARGE

The Pennsylvania State University

The Graduate School

College of Engineering

GUIDED WAVE STRUCTURAL HEALTH MONITORING FOR LARGE

DIAMETER STORAGE TANK FLOORS

A Thesis in

Engineering Science and Mechanics

by

Russell Love

2017 Russell Love

Submitted in Partial Fulfillment

of the Requirements

for the Degree of

Master of Science

May 2017

ii

The thesis of Russell Love was reviewed and approved* by the following:

Joseph L. Rose

Paul Morrow Professor of Engineering Science and Mechanics

Thesis Advisor

Bernhard R. Tittmann

Schell Professor of Engineering Science and Mechanics

Clifford J. Lissenden

Professor of Engineering Science and Mechanics

Judith A. Todd

P.B. Breneman Department Head Chair

Engineering Science and Mechanics

*Signatures are on file in the Graduate School

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ABSTRACT

The goal of this research is to develop a structural health monitoring (SHM) technique for

inspecting large diameter storage tank floors. Current inspection techniques for storage tank

floors require tank shutdown and the tank to be drained of its product and cleaned prior to

inspection. Prior to shutting down the tank and conducting an inspection, little information is

available about the tank floor condition. In some cases, corrosion or other damage causes leaks in

the floor which are not detected until the product is observed on the ground outside the tank.

Therefore, there is a great need for developing a real time SHM system that can detect damage

while the tank is in service. To achieve this, a tomography based SHM approach that achieves full

tank floor coverage was developed. A 37 ft. diameter tank floor mock-up was designed and

fabricated. The floor was constructed from 4’ x 8’ x 5/16” steel plates that were lap welded

together. The mock-up included a 6” chime plate that was welded near the outside of the floor.

Guided wave actuators/receivers were mounted around the outside of the plate in a circular

pattern. Excellent penetration power and signal-to-noise ratios were achieved through all of the

sensor array paths. Defects were introduced to the mock-up starting with a simulated 6” x 12”

corrosion patch. Tomographic images were generated which easily detected the small corrosion

patch. Several different features and signal gating options were explored to optimize the

detectability of the damage. The defect sizes were increased incrementally to explore the defect

sizing capabilities. An excellent linear trend was achieved using a frequency based feature for

defect detection.

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TABLE OF CONTENTS

List of Figures .......................................................................................................................... vi

List of Tables ........................................................................................................................... xii

Acknowledgements .................................................................................................................. xiii

Chapter 1 Introduction ............................................................................................................. 1

Problem Statement and Proposed Solution ...................................................................... 1 Above Ground Storage Tanks .......................................................................................... 2 Failure Mechanisms ......................................................................................................... 3 Current Inspection Techniques ........................................................................................ 5

Visual Inspection ...................................................................................................... 5 Manual Tank Floor Scanners ................................................................................... 7 Robotic Tank Floor Scanners ................................................................................... 9 Acoustic Emissions .................................................................................................. 10

Literature Review ............................................................................................................. 11

Chapter 2 Theory ..................................................................................................................... 14

Brief Overview of Ultrasonic Guided Waves .................................................................. 14 Ultrasonic Guided Wave Tomography ............................................................................ 17 RAPID Technique ............................................................................................................ 19 Guided Wave Features for Tomographic Imaging ........................................................... 23

Time Domain ........................................................................................................... 23 Frequency Domain ................................................................................................... 27

Chapter 3 Experimental Approach ........................................................................................... 32

Guided Wave Mode Selection ......................................................................................... 32 Sensor Design .................................................................................................................. 35 Guided Wave Signal Conditioning .................................................................................. 38

Signal Averaging ...................................................................................................... 38 Software Filtering ..................................................................................................... 39 Image Thresholding.................................................................................................. 39 Dynamic Signal Gating ............................................................................................ 40 Signal Amplitude Compensation ............................................................................. 41 Normalized Tomography Image .............................................................................. 43 Temperature Compensation ..................................................................................... 46

Data Acquisition Hardware .............................................................................................. 48 UltraWave LRT ........................................................................................................ 48 SEEKER ................................................................................................................... 50

Software ........................................................................................................................... 51 Data Acquisition Software ....................................................................................... 51 CT Imaging Software ............................................................................................... 54

v

Chapter 4 Storage Tank Floor Mock-up Experiments ............................................................. 56

Experimental Setup .......................................................................................................... 56 Storage Tank Floor Mock-up Construction .............................................................. 56 Guided Wave Sensor Network ................................................................................. 58 Defect States ............................................................................................................. 60 Acquisition Settings ................................................................................................. 62

Experimental Results ....................................................................................................... 63 Results on Smallest Defect Using the Frequency Ratio ........................................... 63 Results on All Defect States Using the Frequency Ratio ......................................... 67 Results Comparing Different Features on the Small Defect .................................... 73 Results comparing Different Features on all the Defect States ................................ 78 Liquid and Sediment Loading Results ..................................................................... 86 Sparse Array Results ................................................................................................ 88

Chapter 5 Concluding Remarks ............................................................................................... 92

Thesis Summary ............................................................................................................... 92 Future Work ..................................................................................................................... 93

References ................................................................................................................................ 95

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LIST OF FIGURES

Figure 1-1. Photograph of above ground storage tank field. Photograph obtained from

www.gwultrasonics.com [1]. ........................................................................................... 2

Figure 1-2. Photographs of corrosion found on storage tank floors. Photographs obtained

from Hoyle [2]. ................................................................................................................ 4

Figure 1-3. Photograph of a crack being identified with the use of dye penetrant testing.

Photograph obtained from www.premierndtservices.com [4]. ........................................ 6

Figure 1-4. Illustration of magnetic particles being used to detect cracks. Illustration

obtained from Worman [5]............................................................................................... 7

Figure 1-5. Photograph of an operator using a manual MFL scanner to inspect the storage

tank floor. Photograph obtained from www.silverwingndt.com [6]. ............................... 8

Figure 1-6. Illustration of a robotic scanner inspecting an in service storage tank floor.

Illustration obtained from www.mechatronics.mit.edu [8]. ............................................. 9

Figure 1-7. Illustration showing a storage tank outfitted with AE sensors for detecting

active corrosion. Illustration obtained from www.idinspections.com [10]. ..................... 10

Figure 1-8. Example defect map created from received AE events of active corrosion.

Image obtained from www.attar.com.au [11]. ................................................................. 11

Figure 2-1. Comparison of bulk wave excitation (top), and guided wave excitation

(bottom). Utilizing a bulk wave transducer (top) to inspect the region directly under

the transducer, and a bulk wave transducer with an angle beam wedge (bottom) to

inspect the area away from the transducer. Note, this is only one of many methods of

exciting a guided wave. The guided wave is capable of detecting reflections from the

corrosion patch that is distant from the transducer location, which is not possible

with traditional bulk wave techniques. ............................................................................. 15

Figure 2-2. A guided wave dispersion curve of a 0.375"-thick 6061 aluminum plate,

which describes the relationship between wave velocity and frequency for all of the

possible guided wave modes in the structure from 0 – 500 Hz. The SH0 mode is

highlighted in red. ............................................................................................................ 16

Figure 2-3. Common sensor array networks: (a) rectangular, (b) linear, (c) circular, (d)

semi-random..................................................................................................................... 17

Figure 2-4. (Left) Signal paths for two different sensor pair combinations. No structural

damage is present in sensor path A-B. Corrosion is present in sensor path A-C. (Top

right) collected signals from sensor path A-B. The received signals are almost

identical. (Bottom right) collected signals from sensor path A-C. The received signal

is very different from the original baseline signal............................................................ 18

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Figure 2-5. (Left) photograph of a linear sensor array installed on the inside section of an

E-2 wing. (Middle) Photograph of simulated corrosion patch located on opposite

side of the wind. (Right) An excellent CT image is produced when comparing the

baseline data set to the post defect data set. ..................................................................... 19

Figure 2-6. Concept of the probabilistic distribution used in the RAPID reconstruction

algorithm. Note, the colored areas provide different weighted probabilities that a

defect is more or less likely to be present. ....................................................................... 20

Figure 2-7. (Left) A series of simulated elliptical areas of probability between two sensor

paths with different shape factors, β. (Right) Generated CT images using the same

settings and feature, but with different shape factors (top) 1.01, (middle) 1.04, and

(bottom) 1.09. ................................................................................................................... 22

Figure 2-8. Simulated time-domain waveforms. The blue waveform represents the

received signal before damage and the red waveform represents the signal after

damage. The SDC feature value between the two signals is 0. ........................................ 24



damage. The SDC feature value between the two signals is 0.45. ................................... 24



damage. The SDC feature value between the two signals is 1. ........................................ 25

Figure 2-11. Simulated enveloped time-domain waveforms. The blue waveform

represents the received signal before damage and the red waveform represents the

signal after damage. The amplitude-ratio feature value between the two signals is

0.2. .................................................................................................................................... 26

Figure 2-12. Simulated time-domain waveforms that have been squared. The blue

waveform represents the received signal before damage and the red waveform

represents the signal after damage. The energy feature value between the two signals

is 0.36. .............................................................................................................................. 27

Figure 2-13. (Left) Simulated time-domain signals. (Right) Calculated frequency

spectrum for each set of signals. (Top) No phase shift or time delay added to second

signal. (Bottom) Phase shift and time delay was added to second signal. The

frequency spectrum for each set with and without phase shifts and time delays are

identical. ........................................................................................................................... 28



damage. ............................................................................................................................ 29

Figure 2-15. Calculated frequency spectrum for simulated time-domain signals in Figure

2-14. ................................................................................................................................. 29

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Figure 2-16. Calculated frequency spectrum for simulated time-domain signals. The

frequency-ratio was computed using the pulsing frequency 55 kHz. For this example

the frequency-ratio feature value is 1.91. ......................................................................... 30

Figure 3-1. Group velocity dispersion curves for the storage tank floor mock-up. ................. 33

Figure 3-2. Finite element model showing the S0, A0, and circumferential modes traveling

across a simple plate. ....................................................................................................... 34

Figure 3-3. (Top) predicated guided wave signal from finite element model. (Bottom)

actual guided wave data collected on plate. Notice that the predicted and actual

guided wave signals correlate almost exactly. Notice, the S0 is masked by the signal

noise in the experimental data. ......................................................................................... 35

Figure 3-4. Photograph showing both the original sensor and new low-profile sensor. .......... 37

Figure 3-5. Schematic of the new sensor design showing the overall dimensions. ................. 38

Figure 3-6. Generated CT images (left) without thresholding and (right) with

thresholding. ..................................................................................................................... 40

Figure 3-7. An example waveform showing how multiple gates can be used to identify

different wave modes and travel paths in a received signal. ............................................ 41

Figure 3-8. Example guided wave signals (top) without gain compensation and (bottom)

with gain compensation. .................................................................................................. 42

Figure 3-9. (Left) rendering of all the array paths generated with the 40 sensor circular

array. (Right) Generated CT image of the energy distribution due to an imbalanced

distribution of signal path intersections across the monitoring area. ............................... 44

Figure 3-10. Generated CT image of the small 12in x 6in corrosion type defect without

normalization. .................................................................................................................. 45

Figure 3-11. Generated CT image of the small 12in x 6in corrosion type defect with

normalization. .................................................................................................................. 46

Figure 3-12. (Left) CT image generated without temperature compensation. (Right) CT

image generated with temperature compensation. ........................................................... 47

Figure 3-13. Photograph of the 16 channel phased array Olympus UltraWave LRT data

acquisition system [27]. ................................................................................................... 49

Figure 3-14. Photograph of the 16 channel multiplexed Guidedwave SEEKER data

acquisition system [28]. ................................................................................................... 50

Figure 3-15. Screen capture of the sensor array wizard. .......................................................... 52

Figure 3-16. Screen capture of the hardware setup and calibration tab. .................................. 53

ix

Figure 3-17. Screen capture of the signal amplitude compensation and data acquisition

window. ............................................................................................................................ 54

Figure 3-18. Screen capture of the CT imaging tab. ................................................................ 55

Figure 4-1. Photograph of the storage tank floor mock-up. ..................................................... 56

Figure 4-2. Photograph of the chime plate. .............................................................................. 57

Figure 4-3. Schematic of the storage tank floor mock-up. ....................................................... 57

Figure 4-4. Sensor mounting locations on the storage stank floor chime plate. ...................... 58

Figure 4-5. Sensor array paths for (left) 40 sensors and (right) 20 sensors. ............................ 59

Figure 4-6. Sensor array network group sets used to monitor the storage tank floor mock-

up. ..................................................................................................................................... 60

Figure 4-7. Photographs showing every defect state that was introduced to the storage

tank floor. ......................................................................................................................... 62

Figure 4-8. (Left) rendering of the storage tank floor, showing to scale, the size of the

corrosion patch defect relative to the size of the tank floor. Note, the defect is 6in x

12in, and is less than 0.05% of the total area of the tank floor. (Right) photograph of

the corrosion patch defect. ............................................................................................... 64

Figure 4-9. CT image using the frequency ratio feature of the 6in x 12in corrosion patch

defect. Data used in feature extraction was collected using the multi-channel

UltraWave LRT system. .................................................................................................. 65


defect. Data used in feature extraction was collected using the multiplexed Seeker

system. ............................................................................................................................. 66

Figure 4-11. CT image results at each defect state using the frequency-ratio feature using

the data collected with the UltraWave LRT system. As the defect state progressed

from a 6in x 12in simulated corrosion patch to a 4ft x 8ft hole, the frequency-ratio

feature value increased. .................................................................................................... 69


defect. Data used in feature extraction was collected using the UltraWave LRT data

acquisition system. ........................................................................................................... 70

Figure 4-13. CT image results at each defect state using the frequency-ratio feature using

the data collected with the Seeker system. As the defect state progressed from a 6in

x 12in simulated corrosion patch to a 4ft x 8ft hole, the frequency-ratio feature value

increased........................................................................................................................... 72

x


defect. Data used in feature extraction was collected using the UltraWave LRT data

acquisition system. ........................................................................................................... 73

Figure 4-15. (Left) rendering of the tank floor showing to scale the size of the corrosion

patch defect relative to the size of the tank floor. Note, the defect is 6in x 12in, and

is less than 0.05% of the total area of the tank floor. (Right) photograph of the

corrosion patch defect. ..................................................................................................... 74

Figure 4-16. CT images generated using the time-domain features. Data was collected

with the UltraWave LRT system. ..................................................................................... 75

Figure 4-17. CT images generated using the time-domain features. Data was collected

with the Seeker system. .................................................................................................... 76

Figure 4-18. CT images generated using the frequency-domain features. Data was

collected with the UltraWave LRT system. ..................................................................... 77

Figure 4-19. CT images generated using the frequency-domain features. Data was

collected with the Seeker system. .................................................................................... 78

Figure 4-20. Generated CT images for the earliest defected detection for the time-domain

features. The data was collected with the UltraWave LRT data acquisition system.

(Top) SDC feature on 8ft x 4ft hole. (Middle) Amplitude-ratio feature on 12in x 6in

corrosion. (Bottom) Energy feature on 12in x 6in hole. .................................................. 80

Figure 4-21. Generated CT images for the earliest defected detection for the time-domain

features. The data was collected with the Seeker data acquisition system. (Top) SDC

feature on 4ft x 2ft hole. (Middle) Amplitude-ratio feature on 6ft x 3ft hole.

(Bottom) Energy feature on 8ft x 4ft hole. ....................................................................... 81

Figure 4-22. Generated CT images for the earliest defected detection for the frequency-

domain features. The data was collected with the UltraWave LRT data acquisition

system. (From Top to Bottom) Frequency-ratio feature on 12in x 6in corrosion,

frequency-centroid feature on 12in x 6in corrosion, frequency correlation coefficient

feature on 12in x 6in corrosion, and energy feature on 2ft x 1ft hole. ............................. 83

Figure 4-23. Generated CT images for the earliest defected detection for the frequency-

domain features. The data was collected with the Seeker data acquisition system.

(From Top to Bottom) Frequency-ratio feature on 12in x 6in corrosion, frequency-

centroid feature on 2ft x 1ft hole, frequency correlation coefficient feature on 2ft x

1ft hole, and energy feature on 8ft x 4ft hole. .................................................................. 84

Figure 4-24. Storage tank floor mock-up filled with water. .................................................... 86

Figure 4-25. CT image result on the liquid filled storage tank mock-up. ................................ 87

Figure 4-26. A-scan results from sensor pair 3 and 19 showing the received signal on the

tank floor mock-up without water (red) and with water (blue). ....................................... 87

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Figure 4-27. (Left) rendering of all array paths generated with the 40 sensor circular

array. (Right) Generated CT image of the 4ft x 2ft hole using all sensors. ..................... 89

Figure 4-28. (Left) rendering of array paths excluding sensors 1 and 27. (Right)

Generated CT image of the 4ft x 2ft excluding sensors 1 and 27. ................................... 89

Figure 4-29. (Left) rendering of array paths excluding sensors 1, 6, 9, 27, and 32. (Right)

Generated CT image of the 4ft x 2ft excluding sensors 1, 6, 9, 27, and 32. .................... 90

Figure 4-30. (Left) rendering of array paths using only 20 sensors. Every other sensor

was skipped in the 40 sensor array. (Right) Generated CT image of the 4ft x 2ft

using only 20 sensors ....................................................................................................... 91

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LIST OF TABLES

Table 4-1. List of data acquisition settings. ............................................................................. 63

Table 4-2. List of plate temperatures. ...................................................................................... 82

Table 4-3. Summary of smallest size defect detected with the different features and data

acquisition systems. ......................................................................................................... 85

xiii

ACKNOWLEDGEMENTS

I would like to thank my advisor Dr. Joseph Rose for his guidance through the course of

my Bachelor's and Master’s degree programs at Penn State. I have been a student of Dr. Rose’s

now for almost ten years and he has always provided me with wisdom and encouragement

through my Master’s degree program while maintaining a full time career.

I would like to thank Guidedwave for building the storage tank floor mock-up and

allowing me access to their facilities and equipment as well as all of the employees that assisted

me with theoretical calculations, mock-up design, mock-up fabrication, sensor development, and

sensor installation. Specifically I would like to acknowledge the assistance of Cody Borigo and

Steve Owens for all their contributions throughout my thesis work.

Finally, I would like to thank my wife, Allie Love, for her continued support,

reassurance, and understanding over the course of my thesis work. She has had to endure my

absence and occasional complaining while still offering words of encouragement. Without her

support I may have never completed this thesis.

1

Chapter 1 Introduction

Problem Statement and Proposed Solution

The goal of this research is to develop a structural health monitoring (SHM) technique for

inspecting large diameter storage tank floors. Current inspection techniques for storage tank

floors require tank shutdown and the tank to be drained of product and cleaned prior to



the floor which are not detected until its product is observed on the ground outside the tank.


while the tank is in service. To achieve this, a tomography based SHM approached that achieves

full tank floor coverage was developed. A 37 ft. diameter tank floor mock-up was designed and

fabricated. The floor was constructed from 4’ x 8’ x 5/16” steel plates and lap welded together.

The mock-up includes a 6” chime plate welded near the outside of the floor. Guided wave

actuators/receivers will be mounted around the outside of the plate in a circular pattern. Defects

will be introduced to the storage tank floor mock-up in incremental steps starting with a simulated

6” x 12” corrosion patch. Tomographic images will be generated with several different features

and signal gating options to determine the optimal detectability and sizing of the damage.

2

Above Ground Storage Tanks

Above ground storage tanks are used in many different industries including

petrochemical, agricultural, bioenergy, industrial liquids, potable water, etc. A photograph taken

of a storage tank field is shown in Figure 1-1. Storage tanks can hold both solid and liquids of

different viscosities. The contents in a storage tank are under little to no pressure. Compressed

gases and liquids are stored in pressure vessels and are regulated separate from storage tanks.

Storage tanks that store petroleum or other hazardous waste are subject to federal regulation,

which includes the safety, management, maintenance, and inspection of the storage tanks.

Figure 1-1. Photograph of above ground storage tank field. Photograph obtained from

www.gwultrasonics.com [1].

The most common storage tank designs are cylindrical in shape, have flat floors, and

have either fixed or floating roofs. Sizes can range from tens of feet to hundreds of feet in

diameter. The construction is typically from carbon steel due to cost, availability, and ease of

3

fabrication. To aid in corrosion resistance, the carbon steel is protected with a corrosion resistant

paint. In addition, cathodic protection is often applied to storage tanks, where a sacrificial anode

is used to minimize the corrosion of the storage tank. The storage tank floor is created by

overlapping the steel plates and lap welding them together.

Failure Mechanisms

The most common failure mechanism of the storage tank floor is corrosion. Corrosion

damage to the tank floor can cause the contents of the tank to leak into the surrounding area. If

the damage to the floor is severe, catastrophic failure can result from the weakened foundation.

Most storage tanks are fabricated from carbon steel due to cost, availability, and ease of

fabrication. However, carbon steel has limited corrosion resistance. To aid in corrosion resistance,

the carbon steel is protected with a corrosion resistant paint. In addition, cathodic protection is

often applied to storage tanks, where a sacrificial anode is used to minimize the corrosion of the

tank floor [2,3]. These corrosion mitigation systems are not foolproof and corrosion can still

occur. Therefore, regular inspections of the storage tank integrity are necessary.

Corrosion can occur anywhere on the tank floor. Some areas are worse than others. For

instance, corrosion can be underneath the tank floor. Corrosion in this location is not detectable

during a visual inspection. Only, nondestructive techniques would be capable of detecting

corrosion in this location. Corrosion around welds is common. Protective coatings may not

perfectly cover the weld area which creates initiation sites for corrosion. Sediment tends to build

up around the lap welds, which can increase the possibility of corrosion. Depending on the

storage tank foundation and contents stored in the tank, corrosion can be accelerated. Two

examples of corrosion on storage tank floors are shown in the photographs Figure 1-2.

4

Figure 1-2. Photographs of corrosion found on storage tank floors. Photographs obtained from

Hoyle [2].

5

Damage to the storage tank floor can also be caused by fatigue cracking. Many of the

storage tanks are very large and are subjected to various wind loads and vibration. Cyclic stresses

also occur during the fill and empty cycles of contents to and from the tank. During these cycles,

large pumps are running which increase cyclic stresses due to vibrations. After many vibrational

cycles, fatigue cracking can occur. However, in the tank floor, fatigue cracking is less prominent

than corrosion. Therefore, optimizing detection of corrosion is priority [2].

Current Inspection Techniques

Below is an overview of the current inspection techniques employed for storage tank

floors. These inspection techniques include visual, scanning, robotic scanning, and acoustic

emission techniques.

Visual Inspection

When the storage tank is empty and decontaminated, the first assessment of the storage

tank floor integrity is done with a visual inspection. The tank floor is examined for any areas

where corrosion, holes, or cracks can be seen. These areas are noted, and further investigation and

assessment are performed. Some simple inspection equipment can be used to find smaller areas of

damage during the visual inspection. These methods include dye penetrants and magnetic

particles.

6

Dye penetrant testing is performed by applying dye onto the inspection area. The excess

dye is wiped away and a developer is applied to draw out any dye that may have entered any

flaws. Figure 1-3 shows an example of a crack being identified with the dye penetrant method.

Figure 1-3. Photograph of a crack being identified with the use of dye penetrant testing. Photograph

obtained from www.premierndtservices.com [4].

Magnetic particles can also aid in detecting flaws by creating a magnetic field in the

steel, and placing colorful magnetic particles between the magnetic poles. If there is any surface

discontinuity, the magnetic flux will “leak” and attract the magnetic particles as shown in Figure

1-4. If there is a buildup of magnetic particles in a location, than there is most likely a flaw in the

material.

7

Figure 1-4. Illustration of magnetic particles being used to detect cracks. Illustration obtained from

Worman [5].

The visual inspection is very useful in detecting large damaged areas. However,

identifying smaller defects can be very time consuming. For the smaller defects, better inspection

techniques are available. Defects that develop below the tank floor are also undetectable with

visual inspections. From the top, the storage tank floor can look perfect, and below the floor,

there could be severe corrosion damage. Therefore, more thorough and robust inspection

techniques must be applied to ensure the integrity of storage tank floors.

Manual Tank Floor Scanners

Manual floor scanners using bulk wave normal beam ultrasonic inspection or magnetic

flux leakage inspection are the most common methods for inspecting storage tank floors. The

operator will manually push the scanner over the entire tank floor to generate an inspection map.

A photograph of an operator using a manual MFL floor scanner is shown in Figure 1-5. As the

operator pushes the scanner, the scanner detects damage on the tank floor and the location and

severity is saved in the software. Once the scan is complete, a complete map of the tank floor

8

with damaged areas is generated. This allows the operator to easily visualize the damaged

locations for further assessment. Magnetic flux leakage (MFL) and ultrasonic thickness

measurements are the two methods used for detecting damaged areas.

Figure 1-5. Photograph of an operator using a manual MFL scanner to inspect the storage tank

floor. Photograph obtained from www.silverwingndt.com [6].

MFL scanners use a powerful electromagnet to magnetize the steel. The detector is

placed between the two poles of the magnet to detect any magnetic field “leakage” from the steel.

The “magnetic field “leakage” occurs when there is a disturbance in the magnetic field due to

flaws in the steel from corrosion or cracking.

Traditional bulk wave ultrasonic sensors send sound energy into an area directly below

the transducer. Defect detection is made by using gates to track reflections through the thickness

of the inspected material. When wall loss occurs, the arrival time of the sound wave becomes

shorter. This time change can be calculated into material loss by knowing the velocity of sound in

the material. The wall loss can be very accurately measured with this technique [7].

9

Manual scanners generate very accurate damage maps of storage tank floors. The

equipment is relatively affordable and operation is easy. However, scanning requires the storage

tank to be empty and decontaminated. Also, storage tank floors have very large surface areas, and

inspection times can be quite long.

Robotic Tank Floor Scanners

For in situ monitoring of storage tank floors, robotic floor scanners can be utilized.

Robotics scanners are controlled remotely by an operator from outside the tank. An illustration of

a robotic scanning setup is shown in Figure 1-6. The robotic scanners have very similar

inspection probes as the manual scanners. These include both the MFL and ultrasonic probes for

detecting and sizing defects. Robotic scanners must scan the entire area of the tank floor to obtain

full inspection coverage. This leads to long inspection times due to the large tank area.

Figure 1-6. Illustration of a robotic scanner inspecting an in service storage tank floor. Illustration

obtained from www.mechatronics.mit.edu [8].

10

Acoustic Emissions

When corrosion occurs or cracks propagate in a structure, energy is released from the

redistribution of stresses. Part of this energy is released as an ultrasonic stress wave known as an

acoustic emission (AE) event. These ultrasonic signals can be detected, and the location,

frequency, and strength of the signal can be measured.

With the appropriate AE equipment, a storage tank can be outfitted with AE sensors and

the tank floor can be monitored while in service. AE sensors are mounted around the storage tank

to maximize signal detection and AE event location. An illustration of a tank outfitted with AE

sensors on the tank wall is provided in Figure 1-7. During monitoring, AE events are logged and

displayed on a map of the tank floor. As events occur, their locations are plotted on the map.

Areas with high AE activity are identified with a different color or bar graph as shown in Figure

1-8 [9,7].

Figure 1-7. Illustration showing a storage tank outfitted with AE sensors for detecting active

corrosion. Illustration obtained from www.idinspections.com [10].

11

Figure 1-8. Example defect map created from received AE events of active corrosion. Image

obtained from www.attar.com.au [11].

AE tank floor inspections have been found to be very effective in detecting active

corrosion on tank floors. The inspection can be performed while the tank is in service. However,

if the corrosion is not active, no AE events will occur, which mean no detection is possible. Also,

depending on background noise, false detections and AE event masking can occur. If corrosion is

detected with AE, information about the extent of the damage is limited if not impossible.

Therefore, other inspection techniques must be used to quantify the extent of tank floor damage.

Literature Review

By mandate from different government agencies that oversee above ground storage tanks,

inspection of the entire tank, including the floor, is required. This can often lead to costly

shutdowns when inspecting the tank floor with commercially available products. A manual

12

scanner, like the Floormap3DI, is a commercially available MFL floor scanner produced by

Silverwing that is used to manually map the entire storage tank floor for flaws, which produces a

very detailed defect map of the storage tank floor [6]. However, the tank must be emptied and

decontaminated prior to its use. Whereas, TechCorr provides a product that can inspect the

storage tank floor in situ. A robotic crawler is outfitted with either an MFL or ultrasonic probe.

Then, the probe is sent inside the tank where it can scan the entire floor [12]. Another technique

for monitoring corrosion in situ is an acoustic emission system. TankPac, by Mistras, is a

commercially available AE product developed for this purpose. The product uses AE sensors

around the tank to determine the location and frequency of AE events from around the tank floor

[13].

To decrease expense and inspection time, guided wave ultrasonic techniques have been

used as alternative methods for inspecting the tank floor in situ. One method, employed by SWRI,

utilizes a magnetostrictive device that can be moved along the outside chime plate of the storage

tank to scan for defects inside the tank floor. The probe operates in pulse-echo mode where the

sound is sent out and reflected back to the probe [14]. In a complex structure, data interpretation

can be very difficult with this method. To simplify the data interpretation, guided wave

tomography can be utilized to generate defect maps that look extremely similar to the images

produced by commercially available scanners [15,16,17,18].

The research presented in this thesis will expand on these topics by focusing on

optimizing the feature extraction method for detecting anomalies in the tank floor. Commonly,

the SDC features used to detect changes in the signal, which is derived from the mean-removed

cross-correlation value between two signals. However, there are numerous weaknesses when

using this feature. Most notably, being extremely sensitive to small phase changes which occur

from both the anomaly and environmental factors [19,20,21,25,22]. Therefore, this research will

13

focus of classifying features that are both sensitive to defects and insensitive to environmental

changes, e.g. temperature.

14

Chapter 2 Theory

Brief Overview of Ultrasonic Guided Waves

Ultrasonic guided waves are an attractive solution because they offer many benefits over

traditional bulk waves. The main advantage of guided waves is that they can inspect a large area

from a single sensor location, whereas bulk waves can only inspect the area directly underneath

the sensor and must be scanned over the entire area. Guided waves also provide the ability to

inspect hidden and inaccessible regions of structures, structures under soil, water, coatings,

insulations, and concrete. This is possible because guided waves travel along or between two

physical boundaries of a waveguide. Examples of waveguides include plates, pipes, rails, beams,

and any other structures that have physical boundaries parallel to the wave propagation direction

[23,20].

A comparison of ultrasonic bulk waves and guided waves is made in Figure 2-1.

Traditional bulk wave ultrasonic sensors send sound energy into an area directly below the

transducer. They are used for thickness measurements, defect detection, and material

characterization. The main disadvantage of bulk wave techniques is that to cover a large region,

the probe must be mechanically scanned over the entire area. Furthermore, it is difficult to inspect

hidden or inaccessible structures, such as those under coatings or soil, using bulk wave

techniques.

15

Figure 2-1. Comparison of bulk wave excitation (top), and guided wave excitation (bottom).

Utilizing a bulk wave transducer (top) to inspect the region directly under the transducer, and a

bulk wave transducer with an angle beam wedge (bottom) to inspect the area away from the

transducer. Note, this is only one of many methods of exciting a guided wave. The guided wave is

capable of detecting reflections from the corrosion patch that is distant from the transducer location,

which is not possible with traditional bulk wave techniques.

Guided waves travel along or between two physical boundaries of a waveguide; one

example of such a waveguide is a plate. Guided waves take advantage of these boundaries to

inspect over great distances by creating a resonance condition between the structural boundaries,

which allows the waves to propagate much farther than bulk ultrasonic waves. It is possible to

generate guided waves with different types of vibration and energy distributions through the

cross-section of a structure. Exploiting these characteristics gives a skilled engineer the ability to

create waves that are more or less sensitive to different types of defects and loading conditions.

Different guided wave modes also have different velocity characteristics as a function of

frequency. These are all considerations that can affect the performance of a guided wave system,

The generation of certain guided wave modes at particular frequencies to accomplish

special tasks is scientifically-founded and physically-based. For a given structure, a dispersion

curve, such as the one presented in Figure 2-2, can be generated and wave structure profiles

16

subsequently produced. A dispersion curve shows all of the possible guided wave modes that can

be excited in a particular structure and the relationship between wave mode, frequency, and

velocity. From the dispersion curves, wave structure profiles can be created. The wave structure

profiles show how different types of energy are distributed throughout the thickness of that

structure. For example, all of the energy can be concentrated at the surface or it can be evenly

distributed throughout the thickness. The wave vibration components can be predominantly

compressional, flexural, shear, or some combination of these. A complete overview of ultrasonic

guided waves can be found in the textbook written by Dr. Joseph Rose [23].

Figure 2-2. A guided wave dispersion curve of a 0.375"-thick 6061 aluminum plate, which

describes the relationship between wave velocity and frequency for all of the possible guided wave

modes in the structure from 0 – 500 Hz. The SH0 mode is highlighted in red.

17

Ultrasonic Guided Wave Tomography

The guided wave computed tomography (CT) technique is used to detect and monitor

damage, i.e. corrosion, erosion, or cracking, over a structure using a sparse sensor array

surrounding the area of interest. Total area coverage is achieved by collecting data with a large

subset of sensor pair combinations. Figure 2-3 shows common sensor array networks used in

guided wave tomography. As the guided wave travels through the structure, it will interact with

any structural change. Signals from different sensor pairs are shown in Figure 2-4. As the guided

wave travels through an area with no structural change, the signals are nearly identical. When

traveling through a defect area, the signals show a significant change. This change can be

quantified using a number generated from signal feature extraction algorithms. Once the signal

changes are computed, the location and size can be calculated with the tomography algorithms to

create an easy to interpret image [21,24].

Figure 2-3. Common sensor array networks: (a) rectangular, (b) linear, (c) circular, (d) semi-

random.

18

Figure 2-4. (Left) Signal paths for two different sensor pair combinations. No structural damage is

present in sensor path A-B. Corrosion is present in sensor path A-C. (Top right) collected signals

from sensor path A-B. The received signals are almost identical. (Bottom right) collected signals

from sensor path A-C. The received signal is very different from the original baseline signal.

Baseline data sets are compared with subsequent data sets to produce a signal feature

value that is used when generating CT images of the structural damage. CT images provide

information about the location, size, and severity of any structural changes. CT imaging is not

compromised with a failure of individual transducers due to the sparse array network. An

example of a CT image result is shown in Figure 2-5. A linear sensor array was placed on the

back section of an E-2 wing. Then, baseline data was acquired before a simulated corrosion patch

was placed on the front side of the wing. To compute the CT image, another set of data was

acquired post corrosion patch and compared to the baseline data set. As shown if Figure 2-5, the

damage in the wing is easily detectable in the CT image.

19

Figure 2-5. (Left) photograph of a linear sensor array installed on the inside section of an E-2 wing.

(Middle) Photograph of simulated corrosion patch located on opposite side of the wind. (Right) An

excellent CT image is produced when comparing the baseline data set to the post defect data set.

RAPID Technique

Many CT imaging reconstruction algorithms can be applied to guided wave ultrasound

applications. Most of these algorithms were originally developed for X-ray tomography.

However, the Reconstruction Algorithm for Probabilistic Inspection of Damage (RAPID)

algorithm was purposely developed for guided-wave tomography, which was first reported by

Gao, H., et al. [24]. The RAPID algorithm has excellent sensitivity to localized damage. This is

possible because the RAPID algorithm accounts for wave diffraction using an elliptical location

probability distribution, whereas most algorithms assume a direct wave path. An illustration

depicting the probability distribution is provided in Figure 2-6. The RAPID algorithm uses a

statistical comparisons of the current and previous state of a structure to determine if any

structural change occurred [24,25]. There are many features that can be extracted from a guided

wave signal to determine if a change has occurred to a structure. The signal difference coefficient

20

(SDC) feature is traditionally used in the RAPID algorithm [19,20,21,25,22]. However, this

feature has serious weaknesses, and other features are explored later in this thesis.

Figure 2-6. Concept of the probabilistic distribution used in the RAPID reconstruction algorithm.

Note, the colored areas provide different weighted probabilities that a defect is more or less likely

to be present.

For defect location, the RAPID algorithm sums the probabilistic location from all sensor

pair combinations. The mathematical equation to find the probability, P(x,y), that a defect is at

point (x,y) inside the reconstruction area is as follows:

𝑃(𝑥, 𝑦) = ∑p𝑘(𝑥, 𝑦)

𝑁

𝑘=1

=∑𝐴𝑘 (−1

𝛽 − 1𝑅(𝑥, 𝑦, 𝑥1𝑘 , 𝑦1𝑘 , 𝑥2𝑘, 𝑦2𝑘) +

𝛽

𝛽 − 1)

𝑁

𝑘=1

The term R, is defined as:

𝑅(𝑥, 𝑦, 𝑥1𝑘 , 𝑦1𝑘 , 𝑥2𝑘, 𝑦2𝑘)

=√(𝑥 − 𝑥1𝑘)

2 + (𝑦 − 𝑦1𝑘)2 +√(𝑥 − 𝑥2𝑘)

2 +√(𝑦 − 𝑦2𝑘)2

√(𝑥1𝑘 − 𝑥2𝑘)2 + (𝑦1𝑘 − 𝑦2𝑘)

2

21

The term β, is a shape factor, which controls the size of the ellipse. Therefore, the area of

influence from each sensor pair combination can be controlled. This term must be greater than 1.

When R=1, the probability of a defect lying along the centerline of the sensor pair pk(x,y) will be

maximum. When R≥β, the probability of a defect lying outside or along the perimeter of the

ellipse, pk(x,y) will be zero [24,20].

The shape factor β, can greatly affect the final CT image. When the shape factor is close

to one, the elliptical distribution area is narrower. As the shape factor increases, the elliptical

distribution area increases. The elliptical distribution area for multiple shape factors are provided

in Figure 2-7. When the shape factor is small, the resulting CT image will provide a more

accurate location of the defect. However, if the defect is too small and the sensor density is not

high, the defect may not be detectable at all. At the opposite end, if the shape factor is too large,

the defect looks much larger than it really is and the location may be skewed because the

influence of the defect is being picked up from more sensor array paths. The shape factor effect

on CT image reconstruction is shown in Figure 2-7. Therefore, an appropriate shape factor must

be selected for the type of defects expected and the sensor array density to provide an accurate

defect location and size, while not missing the defect altogether. It would be possible to develop

an algorithm that automatically set the shape factor. One algorithm could start with a large shape

factor and step the number down until the best resolution was achieved. Another algorithm could

compute the shape factor by knowing the ray paths in the sensor array network and using a

percentage overlap of the ellipse to achieve an optimal shape factor. However, for the sensor

density and defect sensitivity expected, a shape factor of 1.04 was chosen for the data analysis in

this thesis. This shape factor provides an excellent compromise of defect sensitivity and accuracy

based on the results provided in Figure 2-7. The other algorithms would need to be developed and

tested before use and is above the scope of this thesis.

22

Figure 2-7. (Left) A series of simulated elliptical areas of probability between two sensor paths

with different shape factors, β. (Right) Generated CT images using the same settings and feature,

but with different shape factors (top) 1.01, (middle) 1.04, and (bottom) 1.09.

23

Guided Wave Features for Tomographic Imaging

Time Domain

Many time domain features can be extracted from the guided wave signals for use in the

CT algorithms for tomographic image reconstruction. For the storage tank floor, many of the time

domain features were eliminated due to the complex guided wave signals that were received. The

storage tank floor construction of lap welds and very long distances create this very complex

guided wave signal. In the received signal, there is a combination of multiple modes, acoustic

dispersion, and multiple reflections from lap welds and other structural features. Therefore only

robust time-domain features that can be applied to non-Gaussian waveforms. The most common

time-domain feature used in guided wave CT imaging is the signal difference coefficient (SDC)

feature. Other features used in this paper include the energy and amplitude-ratio features.

Features like rise time, pulse width, arrival time, etc. are not applicable in this case. However,

they would be viable if the waveform was Gaussian.

SDC Feature

The SDC feature is a time-domain feature that is derived from the mean-removed cross-

correlation value between two signals [19,20,21,25,22]. This produces a value between 0 and 1.

The SDC feature is very susceptible to small changes in phase. However, amplitude changes

where the phase does not change, as shown in Figure 2-8, will give a SDC value of 0, or no

change detected. However, as shown in Figure 2-9, a small change in phase will be detected. In

this case, the SDC feature value is 0.45. Figure 2-10 shows an example where the SDC value is 1.

In this instance, no signal was received after the damaged state. It should be noted that the SDC

feature can be calculated several different ways, and that some may take into account the

24

amplitude change. However, in this thesis, phase and amplitude based features were examined

separately.

Figure 2-8. Simulated time-domain waveforms. The blue waveform represents the received signal

before damage and the red waveform represents the signal after damage. The SDC feature value

between the two signals is 0.



between the two signals is 0.45.

25



between the two signals is 1.

The SDC feature is excellent in detecting small changes in phase. Which makes it an

excellent feature for detecting small damage in simple and small structures like plates that are in

very controlled environments. Small environmental changes, like temperature, produce large

SDC feature values. As temperature changes, so does the guided wave velocity. The structure will

also experience expansion or contraction. These environmental changes will show up as a time

delay, or phase delay, in the received signal. As the guided wave travels farther distances, the

time delay gets larger. These time delays produce phase shifts, which produce large SDC feature

values which may mask any change due to a structural defect. Without compensation routines,

which have their limitations, the SDC feature does not perform well in complex and large

structures that are susceptible to environmental changes, such as the storage tank floor. Other

similarity coefficients might be better than the SDC in complex structures. However, these

features have not been explored in this thesis.

26

Amplitude Ratio

The amplitude-ratio feature is a simple feature that determines the maximum amplitude

difference between two signals. The assumption when using this feature is that the maximum

amplitude of the received waveform is from the dominant guided wave mode. In the tank floor,

the dominant guided wave mode is A0. Since only the maximum value is desired, small phase

shifts due to environmental changes are negligible. However, phase changes caused by defects

are also negligible. However, the damage should reflect and scatter the guide wave energy.

Therefore, less energy will be received providing a low amplitude signal. To calculate the

amplitude ratio, the received waveforms are enveloped, as in Figure 2-11, and the maximum

amplitude of each signal is obtained. Then, the difference between the two signals is found and

divided by the baseline signal to obtain the amplitude-ratio change with respect to the baseline

signal. Using the simulated waveforms in Figure 2-11 as an example, an amplitude-ratio feature

value of 0.2 is calculated.

Figure 2-11. Simulated enveloped time-domain waveforms. The blue waveform represents the

received signal before damage and the red waveform represents the signal after damage. The

amplitude-ratio feature value between the two signals is 0.2.

The amplitude-ratio feature can often be a great feature, even though amplitude it’s self is

a poor feature. However, limitations of the amplitude-ratio feature in the large and complex

storage tank floor are still present. Due to the lap welds, long distances, and environmental

27

changes, the signal is complex and the A0 mode may not always be the strongest mode due to

destructive and constructive interference present in the received waveforms.

Energy (Time Domain)

With a complex guided wave signal, looking at the total received guided wave energy can

be sensitive to detecting damage in the structure. To calculate the energy feature, the signal is

squared and each point is summed to obtain the total energy. Figure 2-12 shows two waveforms

that have been squared. This feature should be less sensitive to small changes in phase. However

due to the complexity of the signal, destructive and constructive interference due to

environmental phase changes may be an issue.

Figure 2-12. Simulated time-domain waveforms that have been squared. The blue waveform

represents the received signal before damage and the red waveform represents the signal after

damage. The energy feature value between the two signals is 0.36.

Frequency Domain

Feature extraction can be performed in the frequency-domain in addition to the time-

domain. Frequency-domain features can provided excellent sensitivity to structural damage and

28

are robust even when environmental conditions and storage materials are a concern. These

conditions produce changes in the time-domain signal as phase shifts, which can mask similar

signal changes from any structural changes. Whereas, in the frequency-domain, the phase shift

does not affect the frequency content of the signal. Therefore the frequency-domain features show

great promise when complex structures in harsh environments are being monitored.

Defect sensitivity is also of concern. When a guided wave impinges on a defected area,

certain frequency components of the guided wave are either scattered, attenuated, or unaffected.

Therefore, monitoring changes in the frequency-domain can be useful for defect detection. As for

sensitivity to environmental factors such as temperature, the frequency-domain is unaffected by

phase shifts and arrival times from velocity changes or structural expansion and contraction.

Figure 2-13 shows an example where a phase shift and time delay was added to the simulated

signals and the same frequency spectrum is computed for each set of signals. This is a very

powerful attribute when monitoring complex structures in harsh environments.

Figure 2-13. (Left) Simulated time-domain signals. (Right) Calculated frequency spectrum for each

set of signals. (Top) No phase shift or time delay added to second signal. (Bottom) Phase shift and

time delay was added to second signal. The frequency spectrum for each set with and without phase

shifts and time delays are identical.

29

To go from the time domain to the frequency domain, the Fourier transform is used. For

example, Figure 2-14 shows two simulated time-domain signals and Figure 2-15 shows the

calculated frequency spectrum. From the frequency spectrum, the red waveform has lower

frequency content compared to the blue waveform. Features like this can be exploited to detect

damage in the storage tank floor. Frequency-domain features that are explored in this paper are

the frequency-ratio, frequency-centroid, frequency-correlation coefficient, and frequency-energy

features.


before damage and the red waveform represents the signal after damage.

Figure 2-15. Calculated frequency spectrum for simulated time-domain signals in Figure 2-14.

30

Frequency Ratio

A feature that is both robust and sensitive to defects is the frequency-ratio feature. The

frequency-ratio is computed by calculating the ratio of high and low frequency energy in the

frequency spectrum. As the guided wave energy impinges on a defect, the high frequency content

is more susceptible to absorption and scattering compared to the lower frequency content.

Therefore, a defect can be detected by tracking the ratio of high-to-low frequency content. For the

results presented in this paper, the pulsing frequency was used as the dividing line between the

high and low frequency components. For example, the pulsing frequency was 55 kHz for the

simulated signals in Figure 2-14. Therefore, the high and low frequency energy was computed on

either side of 55 kHz as shown in Figure 2-16. Then the low-frequency energy is divided by the

high-frequency energy to obtain the frequency-ratio. Next, the difference between the two signal

features is found and then divided by the baseline signal to obtain the frequency-ratio change with

respect to the baseline signal.

Figure 2-16. Calculated frequency spectrum for simulated time-domain signals. The frequency-

ratio was computed using the pulsing frequency 55 kHz. For this example the frequency-ratio

feature value is 1.91.

31

Frequency Centroid

The frequency centroid measures where the centroid of the frequency spectrum is. This

feature is useful for determining at what frequency most of the spectrum energy is located. This

feature usually provides more reliable results instead of finding the frequency at the max spectral

value. The centroid accounts for energy distribution across the entire spectrum making it more

robust. The centroid is calculated as a weighted mean of frequencies present in the signals and is

weighted by the power spectrum density values.

Frequency Correlation Coefficient

Similar to the SDC time-domain feature, the frequency correlation coefficient feature

measures how similar two signals are. The signal difference is calculated using the cross-

correlation. First, the frequency spectrum is calculated between the baseline and post defect data

sets. Then, the cross-correlation is calculated between the two frequency spectrums and a value

between 0 and 1 is calculated. For signals that are very similar, the value will approach 1.

Frequency Energy

The ratio between the total power spectrum densities (frequency spectrum) of each data

set can be used as a feature value. To calculate the energy feature, the power spectrum is summed

to obtain the total energy. Next, the difference between the two signal features are found and

divided by the baseline signal to obtain the frequency energy change with respect to the baseline

signal.

32

Chapter 3 Experimental Approach

Guided Wave Mode Selection

In previous work, many different transducers were used to generate different guided

wave modes at different frequencies with different wave structures to determine the optimal

sensor to use for inspecting the storage tank floor. Penetration power was the most important

attribute for sending a guided wave across the large and complex storage tank floor. Due to the

fact that most storage tank are filled with a fluid, a guided wave mode that was insensitive to

liquid loading would be preferred. However, S0 and shear horizontal modes had difficulty

penetrating across the tank floor. Experiments showed that the A0 mode was excellent at traveling

long distances across the storage tank floor. This also agrees with findings by Cawley and

Huthwaite [18,26]. To generate the A0 mode, large disk transducers were found to be the best

transducer configuration. In the complex tank structure, many structural features cause mode

conversion. Therefore the A0 mode that is received is not pure like it would be in a perfect plate;

there is a combination of many modes that have primarily in-plane displacement. This is achieved

by operating the disk transducer in the radial mode. The in-plane particle displacement is what

allows the waveform to propagate through the liquid filled tank without leaking in the fluid.

The optimal frequency at which to excite a guided wave is extremely important. At

different frequencies, different modes may exist which can make signal interpretation difficult.

For the storage tank floor mock-up, the group velocity dispersion curve is shown in Figure 3-1.

There are infinite number of modes, and each mode velocity is dependent on frequency, which

makes the guided wave dispersive. Sensitivity to a defect is also dependent on frequency. Defects

with different sizes and geometries are more or less susceptible to different wavelengths. For

33

optimal performance, both in penetration power and sensitivity, a pulsing frequency around 45

kHz was found to be ideal. This was proven in both modeling and testing on the storage tank

floor mock-up.

Figure 3-1. Group velocity dispersion curves for the storage tank floor mock-up.

Finite element modeling correlated well with observations from the field trials. The

model was simplified to a single plate with no lap welds. The models showed that most of the S0

energy converted into A0 energy. The model also showed a surface edge wave traveling along the

outer edge. This was also observed on the storage tank floor mock-up. A still image of the finite

element model in Figure 3-2 shows the A0, S0, and the surface edge as they travel through and

around the plate. A comparison of the guided wave signals between the finite element model and

the experimental tank floor are provided in Figure 3-3. Notice, the signals obtain from each

correlation almost exactly. Understanding of the different waves traveling through the tank floor

is very useful when gating the signals to look for signal changes that are directly associated with

34

different wave modes. It is also necessary to track the surface edge wave location, because it will

interfere with the other modes depending on the sensor locations. Currently, no signal processing

techniques were written to handle this case. However, it should be examined further in the future

to possibly improve defect detection and localization.

Figure 3-2. Finite element model showing the S0, A0, and circumferential modes traveling across a

simple plate.

35

Figure 3-3. (Top) predicated guided wave signal from finite element model. (Bottom) actual

guided wave data collected on plate. Notice that the predicted and actual guided wave signals

correlate almost exactly. Notice, the S0 is masked by the signal noise in the experimental data.

Sensor Design

The sensor installed on the storage tank floor was selected prior to this thesis. Many types

of sensors were evaluated to determine which type of sensor would generate a guided wave that

was strong enough to penetrate the large complex tank floor. Many types of transducers including

piezoelectric disks, shear bars, stacks, and impact type transducers were tested. Of the sensors

tested, the 3 inch diameter d31 piezoelectric disk transducer performed the best. On average, the

center frequency of these transducers was around 50 kHz. These transducers were able to provide

the necessary penetration power and sensitivity to structural defects that occur in the storage tank

floor.

36

The first prototype sensors installed on the tank floor were designed to be robust and long

lasting in the harsh outdoor environments. Each sensor was built in place on the tank floor mock-

up. They were mounted on the chime plate with aircraft grade epoxy to ensure a strong and

reliable bond that will not degrade over time. Once the epoxy cured, coax cable was soldered to

each sensor. The cable for each sensor was labeled and ran to a single location near sensor 1. Due

to thermal expansion, large static charges will build up in the large piezoelectric sensors. This

built up charge can cause small skin burns if the cable is handle improperly. Also, damage to the

electronics is possible if the protection circuit is not robust enough. To prevent this, resistors were

soldered between the positive and negative leads of the sensor. This put the resistor in parallel

with the signal wire and sensor. A large 2 mega-Ohm resistor was used to make the impedance

high. Therefore, no signal loss would occur due to loss of input power and no voltage build up

could occur over time. To seal the sensor from the elements, i.e. water, a corrosion resistant

rubber cab was put overtop the sensor and affixed with silicone. To prevent any damage due to

moisture in the air when the rubber housing was installed, silica get desiccants were installed

under the rubber housing. The silica gel desiccants will absorb any moisture in the air and prevent

any corrosion to the sensor.

One new sensor was also installed on the tank floor to acquire signal comparison data and

evaluate it as it is exposed to different environmental conditions. The new sensor is completely

sealed from both water and dust egress. It is made of a corrosion resistant low-profile aluminum

housing. The sensor is prepackaged and is easily installed with epoxy. However, the new sensor

was not part of the 40 sensor array network. A photograph of both the old and new sensors

affixed to the tank floor mock-up are shown in Figure 3-4. A schematic, as shown in Figure 3-5,

shows the overall dimensions of the new low-profile sensor.

37

Figure 3-4. Photograph showing both the original sensor and new low-profile sensor.

38

Figure 3-5. Schematic of the new sensor design showing the overall dimensions.

Guided Wave Signal Conditioning

Signal Averaging

Signal averaging is the first signal conditioning algorithm applied after data acquisition.

Signal averaging is a technique where a user defined set of waveforms are acquired with the exact

same acquisition settings and time reference, then summed together and divided by the number of

39

waveforms acquired. This technique removes much of the random “noise” in the signal. This

technique greatly increases the signal-to-noise ratio, especially in a noisy environment. The

signal-to-noise ratio increases proportionally to the square root of the number of averages.

Software Filtering

Software filters are used to further remove unwanted frequency components that were not

removed from the analog hardware filters. Typically, in ultrasonic data acquisition systems, the

analog hardware filters are set very broad, and have few selections for the high-pass and low-pass

filters. Therefore, to increase signal-to-noise ratio of the signal, narrower band software filters can

be used to remove unwanted frequency content of digitized signal. Commonly used software

filters include the Bessel, Butterworth, Chebyshev, and elliptical filters.

Image Thresholding

To enhance the final CT image, an image threshold value can be applied to remove any

noise or artifacts that result from the image reconstruction process. This creates a very easy to

interpret CT image, as shown in Figure 3-6, and typically narrows the defect location. The

threshold value is typically set slightly below the lowest detectable feature value for the smallest

defect. However, to obtain this value, a series of tests have to be performed to identify the lowest

detectable threshold value for each feature. Another option is to set the threshold value slightly

above the noise floor of the image. For the results provided in this paper, no thresholding was

used to ensure an accurate assessment of the extracted features could be performed.

40

Figure 3-6. Generated CT images (left) without thresholding and (right) with thresholding.

Dynamic Signal Gating

When a non-Gaussian wave packet is received, dynamic signal gating is essential for

extracting the correct information from the signal. For example, in the complex storage tank

floor, both the S0 and A0 modes are generated and received. In addition to the direct path arrival,

the wave travels around the chime plate perimeter, which adds another complexity to the received

signal. Due to all the lap-welded joints, there are additional reflections from mode conversion and

signals reflecting back and forth between the multiple panels. This creates a very complex

waveform that is difficult to analyze. To make this task simpler, dynamic time gates can be used

to extract certain parts of the signal based on the sensor distance and velocity of each wave mode.

The gate is dynamic in the sense that the gate changes with respect to velocity and distance for

each sensor pair. An example of multiple time gates being used to identify different wave modes

and travel paths in a received signal is shown in Figure 3-7.

41

Figure 3-7. An example waveform showing how multiple gates can be used to identify different

wave modes and travel paths in a received signal.

By using dynamic gates, intelligent feature extraction can be performed on the wave

packet of the desired mode. This technique was used for isolating the A0 mode for analysis on the

tank floor. It is also possible to compare the feature values in different gates to one another. This

is very useful when one mode is more or less sensitive to a structural change than the other. The

quality of the generated CT image can be used to determine if the correct gate was applied.

Signal Amplitude Compensation

For scenarios like the storage tank floor, significant wave propagation distances can lead

to very large signal amplitude variations. Therefore, for the same acquisition settings, short sensor

paths will have a large amplitude signal, and long sensor paths will have a small amplitude signal.

This can be seen in the example waveforms shown in Figure 3-8. This is an issue with most

42

guided wave data acquisition systems. The analog-to-digital (A/D) converters do not have

sufficient vertical resolution to properly resolve small amplitude signals. This leads to high A/D

quantization noise. To address this issue, an intelligent receiver gain compensation routine is used

to maximize the vertical resolution of the received signal.

Figure 3-8. Example guided wave signals (top) without gain compensation and (bottom) with gain

compensation.

The receiver gain compensation routine is executed for each sensor pair combination to

ensure optimal A/D resolution. First, the routine acquires data at a default gain value. Then, the

43

received signal is checked to ensure the signal is not clipped. If the signal is clipped, the gain is

lowered and the signal is reacquired and check again. This will occur until the signal is not

clipped or the minimum gain value is reached. If the signal is not clipped, the signal is checked to

see if it is within the optimal signal range. The range used for the storage tank floor experiments

was between 75% and 85% of the digitizer’s vertical range limit. This ensures optimal resolution

and leaves sufficient vertical range available in case the signal strength increases, which ensures

no signal clipping occurs. If the signal is outside this range, the gain value to achieve an

amplitude that is 80% of the vertical range is calculated and applied. The signal is then reacquired

and confirmed to fall within the 75% to 85% range. The maximum hardware gain value is used if

the value is reached even if the signal does not fall within the specified range. Figure 3-8 shows

an example of signals before and after gain compensation was applied. A file is generated that

contains every gain value for each sensor pair in the sensor network array. All future data

acquisitions will be performed with the gain values stored in the gain compensation file.

Normalized Tomography Image

During CT image construction, image artifacts can occur due to an imbalanced

distribution of signal path intersections across the monitoring area. Due to sensor geometry and

density, certain areas in the monitoring area will have more or less signal path intersections. As

seen in Figure 3-9, an uneven energy distribution in the CT image is produced using the 40 sensor

array network. More intersections occur towards the middle of the circular array, which biases the

distribution towards the center.

44

Figure 3-9. (Left) rendering of all the array paths generated with the 40 sensor circular array. (Right)

Generated CT image of the energy distribution due to an imbalanced distribution of signal path

intersections across the monitoring area.

The CT image in Figure 3-10, shows the result on the 12in x 6in corrosion type defect

without normalization. Due to the effects of the signal path energy distribution, the defect

location is biased towards the center of the monitoring area where this distribution is greatest.

This also makes the defect area larger. Depending on the array geometry, defect location, and

sensor density, these effects could be worse.

45

Figure 3-10. Generated CT image of the small 12in x 6in corrosion type defect without

normalization.

To remove the artifacts produced from the signal path imbalance, the image can be

normalized. This is done by generating a CT image with a feature value of unity. This will

produce a CT image of the energy distribution produced by the signal path intersections in the

monitoring area. Then, this CT image is used to divide the CT image generated using the desired

feature. This can be seen in the normalized CT image in Figure 3-11. This image was generated

by dividing the image in Figure 3-10 by the image in Figure 3-9. This will eliminate the artifacts

due to the uneven sensor path intersections, which will produce a more accurate and sensitive CT

46

image. Notice the defect area is smaller and in the correct location once normalization has been

performed.

Figure 3-11. Generated CT image of the small 12in x 6in corrosion type defect with normalization.

Temperature Compensation

Several time-domain features are very sensitive to small variations in the acquired

signals. For defect detection, this is preferable. However, small signal changes caused by

environmental changes can generate large feature values. The most common environmental

changes are from temperature variations. The feature extraction algorithms cannot separate the

47

changes in the signal due to either a change from a defect or from an environmental factor.

Therefore, either robust temperature compensation algorithms or database correlation techniques

must be used.

Temperature compensation algorithms stretch the waveform to account for the velocity

change of the propagating wave mode and the change in diameter of the tank floor [18,21]. Figure

3-12 shows CT images that were generated with and without temperature compensation routines.

The image on the left was generated with no temperature compensation routines and the image on

the right was generated with temperature compensation routines. Notice, there are large image

artifacts in the image generated without temperature compensation, and the defect cannot be

identified. However, with temperature compensation, the defect can clearly be seen as shown in

the CT image on the right. The signal stretch method was successful in eliminating image

artifacts.

Figure 3-12. (Left) CT image generated without temperature compensation. (Right) CT image

generated with temperature compensation.

Correct damage indication

Image artifacts due to temperature effects

(a) (b)

48

Although the temperature compensation algorithms worked, there are limitations on how

well the algorithms will work over larger distances and temperature variations. As the wave

propagates further, the time variation increases. This is also true as the structure contracts or

elongates. Therefore a more robust method is necessary. Using an automated temperature

correlation method, the optimal baseline dataset will be selected for comparison. This is

performed by measuring the temperature of the tank floor with a series of thermistors that were

placed around the diameter and center of the storage tank floor. With this method, many baseline

datasets are collected at different temperatures and stored for future reference. Therefore, future

data sets can be compared to baseline datasets at the correct temperature. This technique was used

for all the analysis on the storage tank floor mock-up

Data Acquisition Hardware

Two different commercially available guided wave data acquisition systems were used

for monitoring the storage tank floor mock-up. The two systems used were the Olympus

UltraWave LRT and Guidedwave Seeker systems. The major difference between the two systems

is that the UltraWave LRT system is a 16 channel phased array system, and the Seeker system is

a 16 channel multiplexed system. By using both systems, the performance of both systems can be

evaluated, and there will be a backup of the data in case it becomes compromised or a system

becomes inoperable.

UltraWave LRT

The Olympus UltraWave LRT platform has 16 active pulser channels with time delay

and amplitude control, and up to 32 lines for receiving. The Olympus UltraWave LRT system,

49

shown in Figure 3-13, was primarily designed for guided wave pipe line inspection. However, it

is capable of being utilized for a wide-range of guided wave applications from phased array for

pipe and plate to tomography for plates and shells.

The system is battery operated, which makes it easily portable. The system has 16 active

pulsers that can supply a tone-burst output at 300 volts peak-to-peak. This provides excellent

guided wave penetration power. The system conveniently connects to a control laptop through an

Ethernet connection. The system also has the capability of receiving on up to 32 channels.

However, due to the complexity of the system, this system may not be optimal for long term

monitoring. With 16 individual channels, every channel must be independently calibrated and

may become an issue in long term monitoring applications.

Figure 3-13. Photograph of the 16 channel phased array Olympus UltraWave LRT data acquisition

system [27].

50

SEEKER

The Seeker platform is a single tone burst pulser and receiver system that is multiplexed

into 16 channels. The system is a portable battery-powered unit that is specially designed for

guided wave applications requiring one pulser. A photograph of the Seeker system is shown in

Figure 3-14. This platform is excellent for tomography applications. This system is significantly

less expensive than the UltraWave platform. Also, the system is less complex. Therefore, over

time, the system should provide more consistent results. This is very important in long term

structural health monitoring applications.

Figure 3-14. Photograph of the 16 channel multiplexed Guidedwave SEEKER data acquisition

system [28].

51

Software

Data Acquisition Software

To collect all the necessary data on the storage tank floor mock-up, custom data

acquisition software was developed. Existing data acquisition software for tomography worked

well in simple structures. However, in complex structures like the storage tank floor mock-up, the

system did not perform well. Existing software packages were found not to be reliable, lacked

advanced collected algorithms, had poor database management, and lacked support for newer

high-end data acquisition hardware. With these software issues and the importance’s and cost of

performing tomography experiments on the storage tank floor mock-up, new software was

developed to ensure the data was collected correctly.

There are many differences between the new the new software package and the existing

software. The biggest difference is the signal amplitude compensation algorithms. These new

algorithms are much more robust and intelligent in determining the optimal settings when the

received signals are complex. Also, the new data acquisition software is capable of controlling

newer high-end data acquisition hardware. The two platforms include both the Olympus

UltraWave LRT system and the Guidedwave Seeker system. The major difference between the

two systems is that the UltraWave LRT can acquire data on 16 channels simultaneously, and the

Seeker can only acquire on one channel, through a multiplexer, at a time.

The key aspects of the data acquisition software are the sensor array setup, hardware

control setup, system calibration, signal amplitude compensation, and data acquisition routines. A

sensor array wizard was created, so sensor locations could be easily defined and modified for

different applications. A screen capture of the array wizard is shown in Figure 3-15. The array

wizard allows the user to select predefined arrays, e.g. circular array, or custom arrays. Figure

52

3-16 shows a screen capture of the hardware setup and calibration tab. In this tab, all the

controllable parameters of the data acquisition hardware can be defined. There is also a real-time

waveform viewer that is used for calibration. By changing any of the controls on this tab, the

software will automatically update the data acquisition hardware, signal processing algorithms,

and displays. This allows the user to quickly establish optimal baseline settings to be used in the

data acquisition algorithms.

Figure 3-15. Screen capture of the sensor array wizard.

53

Figure 3-16. Screen capture of the hardware setup and calibration tab.

Of all the data acquisition algorithms, the signal amplitude compensation and data

acquisition routines are the most important. Without these two algorithms functioning correctly,

the monitoring of the storage tank floor mock-up would not be possible. Figure 3-17 shows a

screen capture of the signal amplitude compensation and data acquisition window. During signal

amplitude compensation, the window allows the user to see the collected signal for each sensor

pair. On the graph, cursors show the dynamic gate and signal amplitude compensation limits.

There is also a progress bar that shows the overall progress through the sensor pair combinations.

Once signal compensation is complete the file is saved and used for all future data collections.

The same window is displayed when data collection is performed. This allows the user to see the

signal as the data is being collected to ensure there is no issue during data collection. Once all the

data is acquired, it is saved to disk for use in the imaging software.

54

Figure 3-17. Screen capture of the signal amplitude compensation and data acquisition window.

CT Imaging Software

Custom CT imaging software had to be developed to incorporate the new features and

dynamic gating algorithms that were lacking in other available software packages. The key

algorithms developed for the CT imaging software included software filtering, dynamic signal

gating, feature extraction, and CT imaging using the RAPID technique.

Once the user selected the two data sets for CT imaging, the data was loaded and

processed through user defined software filters. Next, for each waveform, the wave packet of

interest was extracted using the dynamic signal gating routines. These routines would use the

sensor pair distance and mode velocities to calculate the gate. Then, feature extraction would be

performed on these signal subsets. Features in both the time-domain and frequency-domain were

calculated. The time-domain features included the SDC, total energy, and amplitude-ratio

55

features, and the frequency-domain features included the frequency-ratio, frequency centroid,

correlation coefficient, and total energy features. After feature extraction, the RAPID technique

was used to generate the CT image for each feature. A screen capture showing the CT imaging

tab is shown in Figure 3-18.

Figure 3-18. Screen capture of the CT imaging tab.

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Chapter 4 Storage Tank Floor Mock-up Experiments

Experimental Setup

Storage Tank Floor Mock-up Construction

For the storage tank floor experiments, a 37ft.-diameter mock-up was designed and

fabricated. A photograph of the storage tank floor mock-up is shown in Figure 4-1. The tank floor

was constructed from 4ft x 8ft x 5/16in A36 steel plates that were lap welded together. The lap

welded structure creates a complex waveguide for guided wave propagation. The mock-up

included a 1/2in thick 6” wide chime plate that was welded around the outside of the tank floor. A

photograph showing a close up view of the chime plate is shown in Figure 4-2. The chime plate is

required for welding the OD of the shell and is required for reinforcement of the bottom shell as

the tank expands and contracts. A detailed schematic of the tank floor is shown in Figure 4-3. The

tank floor was built outside on a leveled soil and sand pad that was left exposed to environmental

elements for sensor and SHM technique evaluation.

Figure 4-1. Photograph of the storage tank floor mock-up.

57

Figure 4-2. Photograph of the chime plate.

Figure 4-3. Schematic of the storage tank floor mock-up.

58

Guided Wave Sensor Network

The storage tank floor mock-up was outfitted with sensors that were made of 3 inch

diameter piezoelectric ceramic disks operating in the d31 mode. To ensure sufficient coverage

area, a 40 sensor circular array network was installed. The sensor mounting locations on the

chime plate are shown in Figure 4-4. A 40 sensor array network around the 37 ft. diameter

storage tank floor provide more than adequate coverage area, and allows for some sensors to

become compromised without affecting the final CT image result. Figure 4-5 shows the sensor

array paths on the tank floor mock-up for both a 40 and 20 sensor array network. Notice that the

40 sensor array network produces an almost complete coverage area of the tank floor. Whereas,

the 20 sensor array network leaves large areas with no direct path between the sensors.

Figure 4-4. Sensor mounting locations on the storage stank floor chime plate.

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Figure 4-5. Sensor array paths for (left) 40 sensors and (right) 20 sensors.

The sensors were wired into 5 sensor groups with 8 sensors each as shown in Figure 4-6.

Each sensor group was wired to a 10-pin LEMO connector for easy connection to the data

acquisition systems. The groups of sensors are necessary due to the channel limitations of the data

acquisition systems. Both systems are limited to 16 pulsing channels. Therefore, it is necessary to

switch to different sensor groups during data acquisition. This is a manual operation and the

software prompts the user on how to connect the sensors array groups to the data acquisition

system.

60

Figure 4-6. Sensor array network group sets used to monitor the storage tank floor mock-up.

Defect States

Over the course of several weeks, six defect states were introduced to the storage tank

floor mock-up. The smallest defect was a small simulated corrosion patch that was 12 inches long

and 6 inches wide as shown in Figure 4-7. The defect area covered less than 0.05% of the total

tank floor area. To simulate the corrosion, an oxy-acetylene torch was used to melt the steel floor

and small holes were blasted into the molten steel. There are a few through holes in the corrosion

61

patch and areas where the steel melted and reflowed. The use of the oxy-acetylene torch is not

very precise compared to drilled holes where the spacing and depth can be controlled better.

However, this method of corrosion simulation is much more efficient. It also creates a more

realistic representation of corrosion due to the random geometry and material loss depths.

Following the corrosion defect, a series of through holes were placed in the tank floor. The

corrosion defect served as the center point for all of the defect states. The trough holes were cut

out with the oxy-acetylene torch. The hole sizes ranged from 12in x 6in to 8ft x 4ft as shown if

Figure 4-7. These defects provided an excellent test platform for testing defect sizing and defect

detection with different features.

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Figure 4-7. Photographs showing every defect state that was introduced to the storage tank floor.

Acquisition Settings

Before structural health monitoring data could be acquired on the storage tank floor

mock-up, optimal pulser and receiver settings had to be determined. This was done by optimizing

settings for sensor pairs with the longest path and the shortest path. Once completed, these

settings were used for acquiring the data throughout the experiments. A list of the prominent

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pulser and receiver settings are listed in Table 4-1. These settings were used with both the

UltraWave LRT and Seeker data acquisition systems. The receiver gain setting was the only data

acquisition setting that was different. The gain value was automatically calculated for every

sensor combination using the signal amplitude compensation algorithms, and this was performed

for each data acquisition system.

Table 4-1. List of data acquisition settings.

Setting Name Setting Value

Frequency 45 kHz

Voltage 300 V

Cycles 7

Polarity Positive

Repetition Rate 20 Hz

Sampling Rate 3.125 MHz

Acquisition Length 45 ft

Velocity 0.112 in/us

Vertical Range ± 625 mV

Averages 25

Experimental Results

Results on Smallest Defect Using the Frequency Ratio

For the first defect state, a small 6in x 12in corrosion type defect was introduced to the

tank floor. The small defect is less than 0.05% of the total area of the tank floor. A scaled

rendering of the defect on the tank floor is shown if Figure 4-8.

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Figure 4-8. (Left) rendering of the storage tank floor, showing to scale, the size of the corrosion

patch defect relative to the size of the tank floor. Note, the defect is 6in x 12in, and is less than

0.05% of the total area of the tank floor. (Right) photograph of the corrosion patch defect.

Data was acquired with both the UltraWave LRT and Seeker data acquisition systems.

Then, all the signal features were extracted on the baseline and 6in x 12in corrosion patch data

sets, and the change in the feature value between the two data sets was calculated. Finally, CT

images were generated with data collected with both the UltraWave LRT and Seeker data

acquisition system. Excellent sensitivity to the corrosion patch was achieved using the frequency-

ratio feature. This can be seen in Figure 4-9 for the UltraWave LRT system and Figure 4-10 for

the Seeker system. The higher frequency content of the received signal diminished when the

guided wave traveled through the defect region. This is due to absorption, scattering and mode

conversion when the guided wave impinges onto the defect region. The frequency-ratio feature is

very sensitive to the corrosion patch defect and not sensitive to any of the environmental factors.

The temperature difference between the baseline and corrosion data sets was 7 degrees

Fahrenheit.

65

Figure 4-9. CT image using the frequency ratio feature of the 6in x 12in corrosion patch defect.

Data used in feature extraction was collected using the multi-channel UltraWave LRT system.

66


Data used in feature extraction was collected using the multiplexed Seeker system.

As shown in Figure 4-9 and Figure 4-10, the defect location from the CT image is

slightly skew from the actual defect location. There are a few possible reasons for this shift. The

first is due to the lap welds, as the plate thickness changes so does the velocity of the guided

wave. This velocity change will distort the defect location algorithm. Also, during the sensor

installation, the sensors were not mounted perfectly spaced around the tank floor. Also, the tank

floor is also slightly warped and lifted off the ground near the side the defect is located. It is also

possible that more sensors in the array network may solve the issue by providing more sensor

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array paths, which increases the imaging resolution. These theories must be tested further to

determine why the defect location in the CT image is slightly skewed.

The frequency-ratio feature is very sensitive to the small corrosion defect. Therefore, it

would be possible to detect a smaller defect. Therefore, a series of smaller defects should be

introduced to the tank floor mock-up to determine at which point the defect becomes detectable

above the noise floor.

Results on All Defect States Using the Frequency Ratio

The frequency-ratio feature has provided the greatest sensitivity for defect detection on the

storage tank floor mock-up. For defect sizing to be feasible, the frequency-ratio feature needs to

provide a stronger response as the defect size increases. As the defect state progressed from a 6in

x 12in simulated corrosion patch to a 4ft x 8ft hole, the frequency-ratio feature value did increase.

This can be seen in the CT images generated for each defect state using the data collected with the

UltraWave LRT system in Figure 4-11. Note, all CT images are on the same scales for easy

comparison. The results show that the response of the frequency-ratio feature does increase as the

defect size increases. This can also be visualized in the graph in Figure 4-12, which shows that

there is almost a linear change in the frequency-ratio value as the defect size increases. Note, the

first value is the result on the small corrosion patch and the rest of the values are from holes. The

small corrosion patch induced more changes in the frequency domain than a hole of equivalent size.

68

69

Figure 4-11. CT image results at each defect state using the frequency-ratio feature using the data

collected with the UltraWave LRT system. As the defect state progressed from a 6in x 12in

simulated corrosion patch to a 4ft x 8ft hole, the frequency-ratio feature value increased.

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Data used in feature extraction was collected using the UltraWave LRT data acquisition system.

Very similar results were obtained using the Seeker data acquisition system as shown in

Figure 4-13. The generated CT images show that the frequency-ratio feature value increases with

the defect size. Only the first defect state, the corrosion patch, has a slightly higher feature value.

This is because the frequency response from the corrosion is greater than the equivalent sized

hole. The same linear trend of the frequency-ratio feature observed with the UltraWave LRT is

also observed with the Seeker system as shown in the graph in Figure 4-14.

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72

Figure 4-13. CT image results at each defect state using the frequency-ratio feature using the data

collected with the Seeker system. As the defect state progressed from a 6in x 12in simulated

corrosion patch to a 4ft x 8ft hole, the frequency-ratio feature value increased.

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Data used in feature extraction was collected using the UltraWave LRT data acquisition system.

Results Comparing Different Features on the Small Defect

Comparing the CT images generated with the time-domain features, only in one instance

was the small simulated corrosion defect detectable. Note, the corrosion defect is the smallest

defect and is only 0.05% of the total area of the tank floor. A scaled schematic and photograph of

the defect is shown in Figure 4-15. Only the amplitude-ratio feature collected with the UltraWave

LRT system was sensitive to corrosion detect. The CT images computed with the time-domain

features using the UltraWave LRT data can be seen in Figure 4-16. Notice, only the amplitude-

ratio feature shows the defect in the correct location. The defect was not detectable with the data

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collected with the Seeker system as shown in Figure 4-17. The most common feature used in

guided wave tomography, the SDC feature, was not sensitive to the small corrosion defect. If

anything, the SDC feature performed the worst showing the defect on the complete opposite side

of the tank floor. The time-domain features are too susceptible to the environmental changes,

especially temperature. Even though the data is being collected near the same temperature, the

small changes in the sound velocity are showing up as greater changes than that of the corrosion

defect. Therefore, the SDC and other time-domain features are too susceptible to the

environmental changes, which prove that there is a need for a more robust feature that is sensitive

to the defects and not to the environmental changes.

Figure 4-15. (Left) rendering of the tank floor showing to scale the size of the corrosion patch

defect relative to the size of the tank floor. Note, the defect is 6in x 12in, and is less than 0.05% of

the total area of the tank floor. (Right) photograph of the corrosion patch defect.

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Figure 4-16. CT images generated using the time-domain features. Data was collected with the

UltraWave LRT system.

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Figure 4-17. CT images generated using the time-domain features. Data was collected with the

Seeker system.

Using the frequency-domain features, the frequency-ratio feature correctly detected the

correct location of the corrosion defect with the data collected from both the UltraWave LRT

(Figure 4-18) and the Seeker (Figure 4-19) systems. Additionally, data collected with the

UltraWave LRT system, correctly detected the defect with the frequency centroid and correlation

coefficient features as shown in Figure 4-18. With the Seeker system, only the frequency-ratio

feature was able to detect the corrosion defect. The frequency-ratio feature has shown that it is the

most sensitive feature for detecting the corrosion defect while not being sensitive to the

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environmental changes. The frequency-domain features in general are more robust than the time-

domain features in a complex structures with changing environments. Even though the signals

may shift in time due to the environmental changes, the frequency does not change. This makes

the frequency-domain features very desirable for monitoring complex structures with changing

environmental conditions.

Figure 4-18. CT images generated using the frequency-domain features. Data was collected with

the UltraWave LRT system.

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Figure 4-19. CT images generated using the frequency-domain features. Data was collected with

the Seeker system.

Results comparing Different Features on all the Defect States

The time-domain features showed some promise in detecting the small 12in x 6in

corrosion defect. With data collected with the UltraWave LRT system (Figure 4-20), the

amplitude-ratio feature was able to correctly detect the corrosion defect. The energy feature was

able to correctly identify the next defect state, the 12in x 6in hole. However, the commonly used

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SDC feature was not able to detect any defect until the final 8ft x 4ft hole. Data collected with the

Seeker system was not as sensitive to the smaller defects. The CT images for the Seeker system

are shown in Figure 4-21. The smallest defect size detectable was the 4ft x 2ft hole using the SDC

feature. The sensitivity to the small defects using any of the time-domain features is not sensitive

enough to the small defects to properly monitor defects in storage tank floors.

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Figure 4-20. Generated CT images for the earliest defected detection for the time-domain features.

The data was collected with the UltraWave LRT data acquisition system. (Top) SDC feature on 8ft

x 4ft hole. (Middle) Amplitude-ratio feature on 12in x 6in corrosion. (Bottom) Energy feature on

12in x 6in hole.

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Figure 4-21. Generated CT images for the earliest defected detection for the time-domain features.

The data was collected with the Seeker data acquisition system. (Top) SDC feature on 4ft x 2ft

hole. (Middle) Amplitude-ratio feature on 6ft x 3ft hole. (Bottom) Energy feature on 8ft x 4ft hole.

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The frequency-domain features were more sensitive to the smaller defects compared to

the time-domain features. The most sensitive feature to the corrosion defect was the frequency

ratio. Data collected with both the UltraWave LRT and Seeker systems were able to correctly

identify the corrosion defect with the frequency ratio. Also, the frequency centroid and

correlation coefficient features were able to identify the small corrosion defect using data

collected with the UltraWave LRT system (Figure 4-22). These same features were only able to

detect the 2ft x 1ft hole using data collected with the Seeker system (Figure 4-23). It should be

noted that the largest temperature difference between any of the data sets was 11 degrees

Fahrenheit. The temperature for each defect state and data acquisition unit is provided in Table

4-2.

Table 4-2. List of plate temperatures.

Defect State Unit Plate Temperature

(degF)

Baseline USB 71

Baseline UltraWave 76

1' x 0.5' Corrosion USB 69

1' x 0.5' Corrosion UltraWave 69

1' x 0.5' Hole USB 70

1' x 0.5' Hole UltraWave 74

2' x 1' Hole USB 70

2' x 1' Hole UltraWave 76

4' x 2' Hole USB 72


6' x 3' Hole USB 74


8' x 4' Hole USB 65


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Figure 4-22. Generated CT images for the earliest defected detection for the frequency-domain

features. The data was collected with the UltraWave LRT data acquisition system. (From Top to

Bottom) Frequency-ratio feature on 12in x 6in corrosion, frequency-centroid feature on 12in x 6in

corrosion, frequency correlation coefficient feature on 12in x 6in corrosion, and energy feature on

2ft x 1ft hole.

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Figure 4-23. Generated CT images for the earliest defected detection for the frequency-domain

features. The data was collected with the Seeker data acquisition system. (From Top to Bottom)

Frequency-ratio feature on 12in x 6in corrosion, frequency-centroid feature on 2ft x 1ft hole,

frequency correlation coefficient feature on 2ft x 1ft hole, and energy feature on 8ft x 4ft hole.

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A summary of the smallest defect detectable with the different features for both

acquisition systems are listed in Table 4-3. Summary of smallest size defect detected with the

different features and data acquisition systems.. Overall, the data collected with the UltraWave

LRT system is much more sensitive to the smaller defects. Data collected with the UltraWave

LRT system has more vertical resolution than the Seeker system. Also, the hardware filters are

selectable with the UltraWave unit, which allow for more narrow bandpass filtering. These

factors may contribute to the reduction in noise level in the signal, which eliminates some

imaging artifacts. Prior to the experimental findings, the Seeker system was believed to produce a

more consistent signal because there is only one pulser, receiver, and digitizer. Whereas, the

UltraWave LRT system has 16 independent pulser, receivers, and digitizers. However, the

UltraWave LRT system is clearly producing cleaner signals. This will have to be investigated

further too fully understand why the UltraWave LRT system is providing better results.

Table 4-3. Summary of smallest size defect detected with the different features and data acquisition

systems.

Feature Defect State (USB) Defect State (UltraWave LRT)

SDC 4ft x 2ft Hole 8ft x 4ft Hole

Amplitude Ratio 6ft x 3ft Hole 1ft x 0.5ft Corrosion

Energy (Time-Domain) 8ft x 4ft Hole 1ft x 0.5ft Hole

Frequency Ratio 1ft x 0.5ft Corrosion 1ft x 0.5ft Corrosion

Frequency Centroid 2ft x 1ft Hole 1ft x 0.5ft Corrosion

Frequency Correlation Coefficient 2ft x 1ft Hole 1ft x 0.5ft Corrosion

Energy (Frequency-Domain) 8ft x 4ft Hole 2ft x 1ft Hole

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Liquid and Sediment Loading Results

In practice, storage tanks are full of liquid, therefore, the monitoring technique must work

in these conditions. In previous work, liquid loading tests were performed on the storage tank

floor mock-up with and without liquid. The A0 mode has been proven to provide the maximum

penetration power. However, there is concern of energy leakage into the liquid. To determine if

there is still sufficient penetration power with the A0 mode under liquid loading, the mock-up was

filled with water to simulate the fluid in the storage tank. A photograph of the tank floor mock-up

filled with water can be seen in Figure 4-24. The CT image in Figure 4-25 was generated by

comparing the baseline data to data acquired from a fluid filled tank with a defect. Excellent

defect detection was achieved even with liquid present on the tank floor. The energy loss due to

leakage into the fluid of the A0 mode is very minor as shown in Figure 4-26. Notice, the signal

acquired after the tank was filled with water, the blue waveform, is only slightly attenuated

compared to the signal acquired with no water present, the red waveform. Therefore, in situ

monitoring will be possible with fluid stored in the tank. However, further tests need to be

performed to simulate sediment buildup on the tank floor and how these deposits may affect CT

imaging of the defects.

Figure 4-24. Storage tank floor mock-up filled with water.

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Figure 4-25. CT image result on the liquid filled storage tank mock-up.

Figure 4-26. A-scan results from sensor pair 3 and 19 showing the received signal on the tank floor

mock-up without water (red) and with water (blue).

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Sparse Array Results

A common occurrence with in-situ monitoring, is the failure of one or more sensors in the

array. For long-term monitoring, the sensor array density has to be large enough to handle the loss

of a few sensor for it to be practical. The sensor array paths for the 40 sensor circular array used

on the tank floor mock-up is shown in Figure 4-27. Notice, all of the sensor combinations create a

highly dense coverage area of the entire tank floor. With this coverage density, the CT image in

Figure 4-27 can be generated, which depicts the 4ft x 2ft hole defect with high accuracy and

precision. Figure 4-28 shows the sensor array paths if sensor 1 and 27 were omitted in the scan.

The array path density is almost unchanged when compared to the array density in Figure 4-27.

The CT image (Figure 4-28) generated is almost a direct match to the CT image generated in

Figure 4-27. Even in the unlikely scenario where over 10% of the sensors failed, the chosen

sensor density will still provide enough sensor paths to generate accurate and precise CT images.

An example where over 10% of the sensors have been omitted is shown in Figure 4-29. In this

example, sensors 1, 6, 9, 27, and 32 have been omitted from the 40 sensor circular array.

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Figure 4-27. (Left) rendering of all array paths generated with the 40 sensor circular array. (Right)

Generated CT image of the 4ft x 2ft hole using all sensors.

Figure 4-28. (Left) rendering of array paths excluding sensors 1 and 27. (Right) Generated CT

image of the 4ft x 2ft excluding sensors 1 and 27.

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Figure 4-29. (Left) rendering of array paths excluding sensors 1, 6, 9, 27, and 32. (Right) Generated

CT image of the 4ft x 2ft excluding sensors 1, 6, 9, 27, and 32.

If the sensor density is not large enough, the detection sensitivity and accuracy will be

jeopardized. For example, if a sparse sensor array is created that only has 20 sensors instead of 40

sensors to monitor the tank floor area, the coverage area is greatly reduced compared to the array

with 40 sensors. This can be seen by comparing the sparse array in Figure 4-30 to the full array in

Figure 4-27. The CT image generated from the sparse array as shown if Figure 4-30.The 20

sensor array is less sensitive to the defect, and the accuracy of the defect location is worse.

Therefore, having the optimal sensor density is critical to maintain accuracy and precision even in

the event that a few sensors fail during storage tank floor monitoring.

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Figure 4-30. (Left) rendering of array paths using only 20 sensors. Every other sensor was skipped

in the 40 sensor array. (Right) Generated CT image of the 4ft x 2ft using only 20 sensors

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Chapter 5 Concluding Remarks

Thesis Summary

The goal of this research was to develop a structural health monitoring (SHM) technique

for inspecting large diameter storage tank floors. Current inspection techniques for storage tank

floors require tank shutdown and the tank to be drained of its product and cleaned prior to



the floor which are not detected until the product is observed on the ground outside the tank.


while the tank is in service. To achieve this, a tomography based SHM approach that achieves full

tank floor coverage was developed. A 37 ft. diameter tank floor mock-up was designed and

fabricated. The floor was constructed from 4’ x 8’ x 5/16” steel plates and lap welded together.

The mock-up included a 6” chime plate welded near the outside of the floor. Guided wave

actuators/receivers were mounted around the outside of the plate in a circular pattern. Defects

were introduced to the storage tank floor mock-up in incremental steps starting with a simulated

6” x 12” corrosion patch. Using several different features tomographic images were generated. As

theorized, the frequency-domain features were excellent at detecting the small simulated

corrosion patch while not being susceptible to environmental changes, specifically, temperature.

Of the frequency-domain features, the frequency-ratio feature was the most sensitive to the small

simulated corrosion defect.

The major benefits of the ultrasonic tomography monitoring technique developed in this

thesis includes the following:

Monitoring of storage tanks can be done while the tanks are in service.

93

Excellent penetration power for traversing large diameter storage tanks.

The frequency-domain features are very sensitive to corrosion type defects. In the

mock-up, a corrosion defect with an area less than 0.05% of the total area was

easily detectable.

The frequency-domain features were much less susceptible to environmental

changes.

For defect sizing, the feature amplitude value and defect correlation looks

promising as a viable technique for establishing the defect size in storage tank

floors.

Future Work

Future work for this research should include the following:

Smaller corrosion type defects should be introduced to the storage tank floor mock-up to

determine the threshold of detection. The frequency-ratio feature was very susceptible to

the smallest corrosion defect that was placed in the mock-up. Therefore further tests

should be performed to establish the detectable threshold.

The storage tank mock-up should be outfitted with a complete sensor array network

comprised of the new packaged sensors to establish their performance and reliability

before they are used on an in service storage tank

The monitoring technique developed in this thesis should be evaluated on an in service

storage tank to determine the real world performance.

94

The technique should be evaluated over longer time periods to determine if any

degradation to the sensors or technique occur as the plate is exposed to changing

environmental elements.

For storage tanks without chime plates, the technique will need to be evaluated with the

sensors mounted on the outside wall or inside the tank, if regulations allow.

To more accurately predict the size of the defects from the generated tomographic

images, additional experiments should be performed with more defect types, geometries,

and sizes.

There were several possible theories presented in this thesis as to why the defect location

is skewed in the generated CT images. These theories need to be explored further to

determine the cause, and if any algorithm modification can be made to account for such

issues in the storage tank floor.

95

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GUIDED WAVE STRUCTURAL HEALTH MONITORING FOR LARGE