A Fault Diagnosis Approach for Rail Surface Anomalies Using FPGA in Railways

Abstract

Railway transport is a widely used means of transportation for passenger and cargo transportation. In recent years, more emphasis has been placed on railway transport. With the development of high-speed trains, it has become important for passenger transport. Due to the heavy construction of the train, continuous failures occur in the railway line. Various methods of inspection are available to detect these failures. In case of early fault detection and repair of major accidents can be prevented. In this study, an FPGA based method is proposed for rail surface inspection and fault diagnosis. The proposed method is realized by image processing with FPGA. The image is taken on the railway line with the camera attached to the FPGA development board. Pre-processing is performed on the obtained image. Edge extraction is applied to the image after pre-processing. The rail surface is detected using the image obtained as a result of edge extraction. The proposed method works in real time to monitor and diagnose faults. It detects many defects on the track surface. In addition, the proposed method measures the size of the fault on the rail surface. In this study, FPGA based condition monitoring device was developed. An architecture has been developed for implementing the proposed method with FPGA.  This work using FPGA technology is low cost and fast compared to other methods. The proposed method is quite advantageous because of its real-time operation.

Keywords

• Railway,Rail surface detection,Condition monitoring,Embedded system,Image processing

References

1. E. Karakose, and M.T. Gencoglu, “An analysis approach for condition monitoring and fault diagnosis in pantograph-catenary system,” IEEE International Conference on In Systems, Man, and Cybernetics (SMC), pp. 1963-1968, 2013.
2. Y. Santur, M. Karaköse, and E. Akın, “Learning Based Experimental Approach For Condition Monitoring Using Laser Cameras In Railway Tracks,” International Journal of Applied Mathematics, Electronics and Computers (IJAMEC), 4, pp. 1-5, 2016.
3. O. Yaman, M. Karakose and E. Akin “Improved Rail Surface Detection and Condition Monitoring Approach with FPGA in Railways,” International Conference on Advanced Technology & Sciences (ICAT'17), pp. 108-111, May 2017.
4. Australian Rail Track Corporation “Manual for non-destructive testing of rail,” ARTC, Avustralya, 2009.
5. UIC-712 R. “Rail defects, International Union of Railways (UIC),” Paris, Fransa, 2002.
6. S. Chang, Y.S. Pyun, and A. Amanov, “Wear Enhancement of Wheel-Rail Interaction by Ultrasonic Nanocrystalline Surface Modification Technique,” Materials, vol. 10(2), 188, 2017.
7. R. Clark, “Rail flaw detection: overview and needs for future developments,” NDT & E International, 37(2), pp. 111-118, 2004.
8. M. Karakose, O. Yaman, M. Baygin, K. Murat, and E. Akin, “A New Computer Vision Based Method for Rail Track Detection and Fault Diagnosis in Railways,” International Journal of Mechanical Engineering and Robotics Research Vol. 6, No. 1, pp. 22-17, January 2017.

9. S. Faghih-Roohi, S. Hajizadeh, A. Nunez, R. Babuska, and B. De Schutter, “Deep convolutional neural networks for detection of rail surface defects,” International Joint Conference on In Neural Networks (IJCNN), pp. 2584-2589, 2016.
10. G. Karaduman, M. Karakose, and E. Akin, “Experimental fuzzy diagnosis algorithm based on image processing for rail profile measurement,” 15th International Symposium In MECHATRONIKA, pp. 1-6, 2012.
11. C. Tastimur, H. Yetis, M. Karakose, and E. Akin, “Rail Defect Detection and Classification with Real Time Image Processing Technique,” International Journal of Computer Science and Software Engineering (IJCSSE), Volume 5, Issue 12, 2016.
12. Y. Santur, M. Karakose, and E. Akin, “Random forest based diagnosis approach for rail fault inspection in railways,” National Conference on In Electrical, Electronics and Biomedical Engineering (ELECO), pp. 745-750, 2016.
13. U. Netzelmann, G. Walle, A. Ehlen, S. Lugin, M. Finckbohner, and S. Bessert, “NDT of railway components using induction thermography,” In D. E. Chimenti, & L. J. Bond (Eds.), AIP Conference Proceedings, Vol. 1706, No. 1, 2016.
14. X. Li, B. Gao, W.L. Woo, G.Y. Tian, X. Qiu, and L. Gu, “Quantitative Surface Crack Evaluation Based on Eddy Current Pulsed Thermography,” IEEE Sensors Journal, vol. 17(2), pp. 412-421, 2017.
15. K. Ma, T.F.Y. Vicente, D. Samaras, M. Petrucci, and D.L. Magnus, “Texture classification for rail surface condition evaluation,” IEEE Winter Conference on In Applications of Computer Vision (WACV), pp. 1-9, 2016.
16. M. Steenbergen, and R. Dollevoet, “On the mechanism of squat formation on train rails–Part I: Origination,” International Journal of Fatigue, vol. 47, pp. 361-372, 2013.
17. P.R. Possa, S.A. Mahmoudi, N. Harb, C. Valderrama, and P. Manneback, “A multi-resolution fpga-based architecture for real-time edge and corner detection,” IEEE Transactions on Computers, vol. 63(10), pp. 2376-2388, 2014.
18. Q. Xu, S. Varadarajan, C. Chakrabarti, and L.J. Karam, “A distributed canny edge detector: algorithm and FPGA implementation,” IEEE Transactions on Image Processing, vol. 23(7), pp. 2944-2960, 2014.
19. T.M. Khan, D.G. Bailey, M.A. Khan, and Y. Kong, “Real-time edge detection and range finding using FPGAs,” Optik-Intern. Journal for Light and Electron Optics, vol. 126(17), pp. 1545-1550, 2015.
20. M. Karakose, O. Yaman, I. Aydin, and E. Karakose, “Real-time condition monitoring approach of pantograph-catenary system using FPGA,” IEEE 14th International Conference on In Industrial Informatics (INDIN), pp. 481-486, 2016.
21. G. Chaple, and R.D. Daruwala, “Design of Sobel operator based image edge detection algorithm on FPGA,” International Conference on In Communications and Signal Processing (ICCSP), pp. 788-792, 2014.
22. T. Shi, J.Y. Kong, X.D. Wang, Z. Liu and G. Zheng, “Improved Sobel algorithm for defect detection of rail surfaces with enhanced efficiency and accuracy,” Journal of Central South University, vol. 23(11), pp. 2867-2875, 2016.
23. O. Yaman, M. Karakose, I. Aydin, and E. Akin, “Detection of pantograph geometric model based on fuzzy logic and image processing,” IEEE 22nd In Signal Processing and Communications Applications Conference (SIU), (pp. 686-689, 2014.