DESIGN OF RAIL TRACK FLAW DETECTION SYSTEM USING MATLAB

Presented byESHU SHARMAM.Tech(MEC)

14SCME202001

Under the Supervision ofSWET CHANDAN

Assistant ProfessorSchool of Mechanical Engineering

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CONTENT Introduction Case Studies Literature Review Objectives Vision inspection system Work done so far

Data collection Method selection Coding

Further Work References

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INTRODUCTION Rail track inspection is a necessary task in railway

maintenance and is required to periodically inspect the rail 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. Contd….

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Non-destructive evaluation techniques for rail track inspection had developed. These technologies include ultrasonic and eddy current methods,neither technique 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 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 analyzed automatically using a customized image processing software.

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CASE STUDIES In indian railway 80% accidents were caused by

human 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

Contd….

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LITERATURE REVIEW Esther resendiz and narendra ahuja [1] were proposed

“Automated visual inspection of rail road tracks” in june ,2013 A computer vision system, consisting of field-acquired video

and subsequent analysis, could improve the efficiency of the current methods. Such a system is prototyped, and the following challenges are addressed: the detection, segmentation, and defect assessment of track components whose appearance vary across different tracks and the identification and inspection of special track areas. An algorithm that utilizes the periodic manner in which track components repeat in an inspection video is developed. Results are demonstrated on field acquired images and video

Contd….

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LITERATURE REVIEW Gimy joy and Jyothi R L [2] were proposed “ A Real Time

VIS for Rail Flaw Detection” in august ,2014 This paper presents a real-time VIS for discrete surface

defects of rail heads. VIS comprises the Image Acquisition Subsystem (IAS) and the image analysis subsystem. IAS acquires gray rail images for the surface of a rail head, and the latter processes rail images and detects possible defects. This paper propose the Local Normalization(LN) method to enhance the distinction between defects and background in a rail image. VIS first acquires a rail image by the image acquisition system, and then, it cuts the sub image of rail. Track by the track extraction algorithm. Then, VIS enhances the contrast of the rail image .At last, VIS detects defects using the defect localization based on projection profile (DLBP), which identifies possible defects using the projection profile of the mean intensity over each longitudinal (or transversal) line. This is robust to noise and very fast. Contd….

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LITERATURE REVIEW Ashwini belkhade and Snehal kathale [3]

were proposed “Automatic vision based inspection of railway track-a review” in Indian journal of research in engineering and technology.

This paper proposes a system to inspect the rail track component such as missing bolts, tie plates, anchors etc by using vision based method and simultaneously do the calibration of railway track by using vibration based method. The system provides real-time monitoring and structural condition for railway track using vision based method and calibration to search the fault location on the track. Inspections include detecting defects on tracks, missing bolts, anchor, tie plate and clips etc. In vision based method camera we will use to capture the images or videos. In vibration based method some sensors we will use to detect the vibrations on the railway track.We will extract the signal from 2-D.

Contd….

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LITERATURE REVIEW

Mohd. Karukh Hashmi and Avinash G Keskar [5] were proposed “Computer-Vision Based Visual Inspection And Crack Detection Of Rail Road Tracks” in Recent Advances in Electrical and Computer Engineering.

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 processing the images of Railroad surface defect and cracks. It has mostly been implemented on computers. For better speed and complexity, the algorithms need to be implemented on embedded platforms.

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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 detecting the 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 detection coil 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 defects using 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 of human 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.

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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 analyzed automatically using a customized image processing programe.

Contd….

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DATA ACQUISITION

Digital cameras are used to capture the images or videos of rail track

Surf 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.

Contd….

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DATA ACQUISITION SYSTEM

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IMAGE ANALYSIS The frames of image are proceeds by using

algorithm to 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

Contd….

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WORK DONE SO FAR Data collection

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METHOD SELECTION Here we have so many methods for rail track

fault detection,like music algorithm,neural networks,pixelation and wavelets etc.

We use matlab coding method due to certain causes like easy to understand,fault detection is easy,coding is easy.

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CODING First give the image to the system. Adjust the image. Convert the image from RGB to Gray. We done thresolding operation on image. Then we apply several morphological operation

on image like1. Cleaning of image2. Thinning of image3. Filled image Then we apply image tool for crack detection. At last we give command for crack length

detection.

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CODE load image I=imread('two.jpg'); figure,imshow(I) title('Original image') Image adjust Istrech = imadjust(I,stretchlim(I)); figure,imshow(Istrech) title('Contrast stretched image') Convert RGB image to gray Igray_s = rgb2gray(Istrech); figure,imshow(Igray_s,[]) title('RGB to gray (contrast stretched) ')

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o Image segmentation by thresholdinguse incremental value to run this selection till re-quired threshold 'level' is achieved

level = 0.08; Ithres = im2bw(Igray_h,level); figure,imshow(Ithres) title('Segmented cracks')o Image morphological operation BW = bwmorph(gradmag,'clean',10); figure,imshow(BW) title('Cleaned image') BW = bwmorph(gradmag,'thin', inf); figure,imshow(BW) title('Thinned image') BW = imfill(gradmag, 'holes') figure,imshow(BW) title('Filled image')

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Image tool figure,imtool(BW1) figure,imtool(I) Calaculate crack length calibration_length=0.001; calibration_pixels=1000; crack_pixel=35; crack_length=(crack_pixel *calibration_length)/calibration_pixels;

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FURTHER WORK Another parameter of crack width yet to be

calculated. The image tool work has to be completed

yet. Real time implemention is also in process.

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REFERENCES 1-Esther Resendiz,Member, IEEE, John M. Hart, and Narendra

Ahuja, Felllow IEEE”Automated Visual Inspection of Railroad Tracks” 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 Based Inspection 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 2010

 5-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 .

 

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REFERENCES 6-Abhisekh Jain, Arvind, Balaji, Ram Viyas N.P.” Onboard dynamic

rail track safety monitoring system” International conference on advanced communication systems, January 10 - 12, 2007

7-. Beena vision “Automated Rail Surface and Track Inspection”. 8-Isabelle Tang and Toby P. Breckon, “Automatic road environment

classification” IEEE transaction on intelligent transportation systems, vol.12, no.2, June 2011. 

9-Hoang Trinh Norman Haas Ying Li Charles Otto Sharath Pankanti “Enhanced rail component detection and consolidation for rail track inspection” Ibm T. J. watson research center 19 skylikne dr, hawthorne, ny 10532.

10-Maneesha Singh, Sameer Singh, “Autonomous rail track inspection using vision based system “IEEE international conference on computational intelligence for homeland security and personal safety alexandria, va, usa 16-17 october 2006.

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THANK YOU