Eshu Kaushik Ji1

8/18/2019 Eshu Kaushik Ji1

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DESIGN OF RAIL TRACK FLAW DETECTION

SYSTEM USING MATLAB

Presented by

ESHU SHARMA

M.Tech(MEC)

14SCME202001

Under the Supervision of

SWET CHANDAN

Assistant Professor

School of Mechanical Engineering


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CONTENTIntroduction

Accident summary

Literature Review

Methods used in past

Proposed work

Objectives

Vision inspection system

Work done so farData collection

Method selectionHistogram equalization

Result

Further Work

References 2


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INTRODUCTIONRail track inspection is a necessary task in railway

maintenance and is required to periodically inspect therail track by trained human operator, who is walking along

the track & searching for defects .

The detection of cracks in rails is a challenging problem,and much research effort has been spent in the

development of reliable, repeatable crack detection

methods for use on in service rails.

Rail inspection methods include destructive techniques

and non-destructive techniques, such as hammer

sounding. But these methods just “cover limited space and

have limited effectiveness in identifying the faults.3

Contd….


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Non-destructive evaluation techniques for rail track

inspection had developed. These technologies include

ultrasonic and eddy current methods,neithertechnique is particularly effective for the detection of

cracks in the rail foot. The results of these studies

confirm the ability of the proposed method to locate

and quantify surface-connected notches and cracks

Visual inspection has been developed in recent years

with the great progress of computer vision

techniques. In a visual inspection system (VIS), a

high speed digital camera, which is installed under atest train, is used to capture images of a rail track as

the train moves over the track, and then, the

obtained images are analyzed automatically using a

customized image processing software.4


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ACCIDENT SUMMARY

In indian railway 80% accidents were caused byhuman failure.

Here we have the summary of rail accidents due to

derailment causes from year 2009 to 2014.

YEAR ACCIDENTS 2009-10 80

2010-11 78

2011-12 55

2012-13 48

2013-14 52

5

Contd….


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LITERATURE SURVEY

NAME PROPOSED WORK JOURNAL

1-Esther resendiz andnarendra ahuja

In it the detection,segmentation and defect

assessment is

considered . An algorithm

is developed to inspect

the acquired images

“Automated visualinspection of rail road

tracks”, IEEE

Transactions on

intelligent transportation

systems, vol. 14, no.2,june 2013.

2-Gimy joy and Jyothi

R L

This paper presents areal-

time VIS for discrete

surface defects of rail

heads.This paper propose

the Local

Normalization(LN)

method to enhance the

distinction betweendefects and background

“ A Real Time VIS for

Rail Flaw Detection” ,

International Journal of

Scientific and Research

Publications, Volume 4,

Issue 8, August 2014


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3- Ashwini belkhade and

Snehal kathale

This paper proposes a

system to inspect the

rail track component

such as missing bolts,

tie plates, anchors etc

by using vision based

method. This system

provides real-time

monitoring andstructural condition for

railway track using

vision based method

“Automatic vision

based inspection of

railway track-a review”

in Indian journal of

research in engineering

and technology,Volume:

03 Issue: 02 ,Feb-2014.

4-Mohd. Karukh

Hashmi and Avinash G

Keskar

This paper proposes a rail

surface defects inspection

method based on

computer vision system. Various algorithm related

denoising, filtering,

thresholding;

segmentation and feature

extraction are applied for

“Computer-Vision Based

Visual Inspection And

Crack Detection Of Rail

Road Tracks” in Recent Advances in Electrical and

Computer Engineering,

jan-2012


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METHODS USED IN PAST

EDDY CURRENT CRACK DETECTION METHOD:

It is used to detect discontinuities and defects in conductive materials.

Eddy current inspection system of rail flaws used in this study included a

detection coil and an excitation coil, which formed an eddy current sensor

probe. Two eddy current sensor probes were used. One was for detectingthe signal from a rail. It was positioned on a tested sample and scanned

along the rail length. Another was for reference. It was positioned in air

far from a sample. The controller supplied an excitation current to a

series connection of two excitation coils and amplified a signal from the

detection coils. The width of the railhead was 65 mm; thus, the detectioncoil in the sensor probe could not effectively evaluate the entire plane of

the rail top. Therefore, the position of the sensor probe was varied in five

different positions along the width. The scan speed of the sensor probe

was 2.5 mm/s and the data acquisition rate was 8 point/s (3.2 point/mm).

The frequency ofthe exciting magnetic field was 5 kHz.

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ULTRASONIC CRACK DETECTION METHOD:

Rails are systematically inspected for internal and surface defectsusing various Non-Destructive Evaluation (NDE) techniques.

During the manufacturing process rails are examined visually for

any surface damage, while the presence of any internaldefects is

assessed mainly through ultrasonic inspection.Ultrasonic testing

(UT) is a non-destructive inspection method that uses high

frequency sound waves (ultrasound) that are above the range ofhuman hearing, to measure geometric and physical properties in

materials. To perform UT, electrical energy is converted to

mechanical energy, in the form of sound waves, by a transducer. The

transducer accomplishes this energy conversion due to a

phenomenon referred to as the piezoelectric effect. This occurs in

several materials, both naturally-occurring and manmade. Quartz is

a naturally occurring piezoelectric material. A piezoelectric material

will produce a mechanical change in dimension when excited with

an electronic pulse. Similarly, this same material will also produce

an electric pulse when acted upon mechanically. 10


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PROPOSED WORK

The goal of this project is to design a rail trackflaw detection system using MATLAB.

Here we use MATLAB software,Image processing

tool to get our goal.

Here we use histogram equalization method toachieve our aim.

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OBJECTIVE:

o Reduce rail accidents caused by surface cracks.

1)Proper maintenance of rail tracks.

2)Identify the crack geometry.

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VISION INSPECTION SYSTEM

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VISION INSPECTION SYSTEM

In vision based method our device will capture videos of

railway track component using vehicle-mounted Camera,

image enhancement using image processing and assisted

automation using a real time tracking algorithms.

In a visual inspection system (VIS), a high speed digital

camera, which is installed under a test train, is used to

capture images of a rail track as the train moves over the

track, and then, the obtained images are analyzedautomatically using a customized image processing programe.

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ISITION

Digital cameras are used to capture the images or

videos of rail trackSurf View comes with on board computer, data

acquisition and software along with six cameras

scanners and cables .A calibrated CCTV camera is

used to capture the image frame at resolution 640x480

at 30 frames per second which was mounted on the rail

track

Different types of cameras are used for data

acquisition purpose in different vision based system.

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IMAGE ACQUISITION SYSTEM

16 fig.1-Image acquisition system

Reference- “beena vision system”[7]


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IMAGE ANALYSIS

The frames of image are proceeds by using algorithmto identify the defected component and assess the

stipulation of railway tracks.

In vision based system image processing is used to

recognize of clips, smoothing and edge detection.The captured data send to PC .

Matlab coding program is used for defect analysis and

it provide a comprehensive result evaluation

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WORK DONE SO FAR

Data collection

18

Reference-www.ndt.net,www.iorw.org


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METHOD SELECTION

Here we have so many methods for rail track

fault detection , like music algorithm, neural

networks , pixilation and wavelets etc.

We use MATLAB coding method using histogram

due to certain causes like easy to

understand,fault detection is easy,coding is easy.

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HISTOGRAM EQUALIZATION

Histogram equalization is a simple and effective image

enhancing technique. Histogram equalization is a technique for adjusting image

intensities to enhance contrast.

It is a challenge to inspect rail track defects in a visionsystem because of illumination inequality and the variation

of reflection property of rail surfaces.

Histogram equalization is widely used for contrast

enhancement in a variety of applications due to its simple

function and effectiveness.

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RESULT

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FURTHER WORK

Real time implementation is under process.

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REFERENCES1-Esther Resendiz,Member, IEEE, John M. Hart, and Narendra

Ahuja, Felllow IEEE”Automated Visual Inspection of RailroadTracks” IEEE transaction on intelligent transportation systems,

vol.14, no.2, June 2013

2-Jyothi R L ,Gimy joy “A Real Time VIS For Rail Track Flaw

Detection” International Journal of Scientific and

Research Publications, August 8,2014

3-Ashwini Belkhade and Snehal Kathale, “Automatic Vision BasedInspection Of Railway Track –A Review” International Journal Of

Research In Engineering and Technology .4-Luis Fernando, “Condition Monitoring Of Railway Turnouts And

Other Track Components Using Machine Vision” November 20105-Mohd. Karukh Hashmi and Avinash G. Keskar, “Computer Vision

Based Visual Inspection And Crack Detection Of Rail Road Tracks”

Recent Advances in Electrical and Computer Engineering .6-www.iorw.org,(institute of rail welding).7-www.ndt.net

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REFERENCES

8-Abhisekh Jain, Arvind, Balaji, Ram Viyas N..! "nb#ard dynami$

rail tra$k sa%ety m#nit#rin& system! 'nternati#nal $#n%eren$e #n

advan$ed $#mm(ni$ati#n systems, Jan(ary )* - )2, 2**+

-. Beena visi#n A(t#mated Rail (r%a$e and Tra$k 'nspe$ti#n!.

)*-'sabelle Tan& and T#by . Bre$k#n, A(t#mati$ r#ad envir#nment$lassi%i$ati#n! '/// transa$ti#n #n intelli&ent transp#rtati#n systems,

v#l.)2, n#.2, J(ne 2*)).

))-0#an& Trinh N#rman 0aas 1in& i 3harles "tt# harath ankanti

/nhan$ed rail $#mp#nent dete$ti#n and $#ns#lidati#n %#r rail tra$k

inspe$ti#n! 'bm T. J. ats#n resear$h $enter ) skylikne dr,

hath#rne, ny )*562.

)2-7aneesha in&h, ameer in&h, A(t#n#m#(s rail tra$k inspe$ti#n

(sin& visi#n based system '/// internati#nal $#n%eren$e #n

$#mp(tati#nal intelli&en$e %#r h#meland se$(rity and pers#nal sa%ety

aleandria, va, (sa )9-)+ #$t#ber 2**9.24


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THANK YOU

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Eshu Kaushik Ji1