Research Article
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Year 2016, Special Issue (2016), 307 - 313, 01.12.2016
https://doi.org/10.18100/ijamec.270627

Abstract

References

  • [1] Sawadısavı, S. V. Development of Machine-Vision Technology for Inspectıon of Railroad Track, Graduate College of the University of Illinois at Urbana-Champaign, 2010.
  • [2] Santur, Y. Karaköse, M. Aydın, I. Akın, E. IMU based adaptive blur removal approach using image processing for railway inspection, In 2016 International Conference on Systems, Signals and Image Processing (IWSSIP) (pp. 1-4), 2016.
  • [3] Yaman, O. Karakose, M. Akin, E. PSO Based Diagnosis Approach for Surface and Components Faults in Railways, International Journal of Computer Science and Software Engineering (IJCSSE), vol. 5, pp. 89–96, May. 2016.
  • [4] Xin, L. Markine, V.L. Shevtsov, I. Numerical analysis of rolling contact fatigue crack initiation and fatigue life prediction of the railway crossing, In CM2015: 10th International Conference on Contact Mechanics, Colorado Springs, USA, 30 August-3 September 2015.
  • [5] Johansson, A. Palsson, B. Ekh, M., Nielsen, J.C. Ander, M.K. Brouzoulis, J. Kassa, E. Simulation of wheel–rail contact and damage in switches & crossings, Wear, 271(1), 472-481, 2011.
  • [6] Bocciolone, M. Caprioli, A. Cigada, A. Collina, A. A measurement system for quick rail inspection and effective track maintenance strategy, Mechanical Systems and Signal Processing, 21(3), 1242-1254, 2007.
  • [7] Palsson, B. Optimisation of railway switches and crossings, Chalmers University of Technology, 2014.
  • [8] Qingyong, L. Shengwei, R. A Real-Time Visual Inspection System for Discrete Surface Defects of Rail Heads, IEEE Transactions on Instrumentation and Measurement, Vol. 61, 2012, 2189-2199.
  • [9] Limin, C. Yin, L. Kaimin, W. Inspection of rail surface defect based on machine vision system, 2nd International Conference on Information Science and Engineering (ICISE), 3793 - 3796, 2010.
  • [10] Ying, L. Trinh, T. Haas, N. Otto, C. Pankanti, S. Rail Component Detection, Optimization, and Assessment for Automatic Rail Track Inspection, IEEE Transactions on Intelligent Transportation Systems, Vol. 15, 2014, 760 – 770.
  • [11] Dubey, A., Jaffery, Z. Maximally Stable Extremal Region Marking (MSERM) based Railway Track Surface Defect Sensing. IEEE Sensors Journal, 2016.
  • [12] Babenko, P. Visual inspection of railroad tracks (Doctoral dissertation, University of Central Florida Orlando, Florida), 2009.
  • [13] Li, Q. Shi J. Li, C. Fast line detection method for Railroad Switch Machine Monitoring System, In 2009 International Conference on Image Analysis and Signal Processing, pp. 61-64, 2009.
  • [14] Qi, Z., Tian, Y., Shi, Y. Efficient railway tracks detection and turnouts recognition method using HOG features. Neural Computing and Applications, 23(1), 245-254, 2013.
  • [15] Wang, P. Xu, J. Xie, K. Chen, R. Numerical simulation of rail profiles evolution in the switch panel of a railway turnout, Wear, 2016.
  • [16] Kassa, E. Nielsen, J.C. Dynamic interaction between train and railway turnout: full-scale field test and validation of simulation models, Vehicle System Dynamics, 46(S1), 521-534, 2008.
  • [17] Zwanenburg, W.J. Modelling degradation processes of switches & crossings for maintenance & renewal planning on the Swiss railway network, 2009.
  • [18] Jalili Hassankiadeh, S. Failure analysis of railway switches and crossings for the purpose of preventive maintenance, 2011.
  • [19] Schupp, G. Weidemann, C. Mauer, L. Modelling the contact between wheel and rail within multibody system simulation, Vehicle System Dynamics, 41(5), 349-364, 2004.
  • [20] Karakose M. Yaman, O. Akin E. Detection of Rail Switch Passages Through Image Processing on Railway Line and Use of Condition-Monitoring Approach, International Conference on Advanced Technology & Sciences (ICAT'16), pp. 99-105, Sept 2016.
  • [21] Yaman, O. Karakose, M. Akin, E. Aydin, I. Image processing based fault detection approach for rail surface, In Signal Processing and Communications Applications Conference (SIU), 2015, pp. 1118-1121.
  • [22] Qingyong, L. Shengwei, R. A Real-Time Visual Inspection System for Discrete Surface Defects of Rail Heads, IEEE Transactions on Instrumentation and Measurement, vol. 61, 2012, pp. 2189-2199.
  • [23] Bouchikhi, A. Boudraa, A.O. Cexus, J.C. Chonavel T., Analysis of multicomponent LFM signals by Teager Huang-Hough transform, IEEE Transactions on Aerospace and Electronic Systems, 1222-1233, 2014.
  • [24] Aydin, I. Karakose, E. Karakose, M. Gençoglu, M.T. Akın, E. A new computer vision approach for active pantograph control, In Innovations in Intelligent Systems and Applications (INISTA), 2013 IEEE International Symposium on (pp. 1-5). IEEE, 2013.
  • [25] Karakose, M. Sensor Based Intelligent Systems for Detection and Diagnosis, Journal of Sensors, 2016.

Real time implementation for fault diagnosis and condition monitoring approach using image processing in railway switches

Year 2016, Special Issue (2016), 307 - 313, 01.12.2016
https://doi.org/10.18100/ijamec.270627

Abstract

Today, railway
transportation is one of the transport modes commonly used. Compared to other
transport modes, railway traffic is highly critical. Multiple railway vehicles
run constantly on one or two lines. Rail switch passages are used to prevent
locomotives from colliding with one another and avoid traffic disruptions.
Through switch passages, locomotives pass from one line to another. Friction
between rail and wheels on switch passages is considerably high. This friction
leads to failures on switch passages. Unless these failures are diagnosed early
and remedied, significant accidents emerge.  



In this study, a new
approach based on image processing has been presented for detection of rail
switch passages on railway lines. A test vehicle has been created in order to
test the proposed approach and apply it on a real-time system. Railway line is
monitored by digital cameras fixed on this test vehicle. Image-processing
approach is developed on the real-time images captured from the railway line
and the switch passages on the line are detected. In addition, by specifying
the train route, the fault which occurring at the point of the switches is
detected. The image-processing approach consists of three main parts including
pre-processing, feature extraction and processing of the features obtained. At
the pre-processing stage, the basic image processing methods are used. At the
feature extraction stage, Canny edge extraction algorithm is used and hence the
edges in the image are detected. Hough transform method is used at the stage of
processing of the extracted features. Following Hough transform stage, straight
lines and angles of these lines are obtained on the image. Taking into account
the angle of each straight line, the junction points of the lines are
calculated. Thus, rail switch passage and switch types are detected. The
proposed image-processing approach is highly fast and real time-based. Compared
to the existing studies in the literature, it is seen that the proposed method
gives fast and successful results. This study intends to diagnose the failures
on switch passages early and prevent potential accidents.

References

  • [1] Sawadısavı, S. V. Development of Machine-Vision Technology for Inspectıon of Railroad Track, Graduate College of the University of Illinois at Urbana-Champaign, 2010.
  • [2] Santur, Y. Karaköse, M. Aydın, I. Akın, E. IMU based adaptive blur removal approach using image processing for railway inspection, In 2016 International Conference on Systems, Signals and Image Processing (IWSSIP) (pp. 1-4), 2016.
  • [3] Yaman, O. Karakose, M. Akin, E. PSO Based Diagnosis Approach for Surface and Components Faults in Railways, International Journal of Computer Science and Software Engineering (IJCSSE), vol. 5, pp. 89–96, May. 2016.
  • [4] Xin, L. Markine, V.L. Shevtsov, I. Numerical analysis of rolling contact fatigue crack initiation and fatigue life prediction of the railway crossing, In CM2015: 10th International Conference on Contact Mechanics, Colorado Springs, USA, 30 August-3 September 2015.
  • [5] Johansson, A. Palsson, B. Ekh, M., Nielsen, J.C. Ander, M.K. Brouzoulis, J. Kassa, E. Simulation of wheel–rail contact and damage in switches & crossings, Wear, 271(1), 472-481, 2011.
  • [6] Bocciolone, M. Caprioli, A. Cigada, A. Collina, A. A measurement system for quick rail inspection and effective track maintenance strategy, Mechanical Systems and Signal Processing, 21(3), 1242-1254, 2007.
  • [7] Palsson, B. Optimisation of railway switches and crossings, Chalmers University of Technology, 2014.
  • [8] Qingyong, L. Shengwei, R. A Real-Time Visual Inspection System for Discrete Surface Defects of Rail Heads, IEEE Transactions on Instrumentation and Measurement, Vol. 61, 2012, 2189-2199.
  • [9] Limin, C. Yin, L. Kaimin, W. Inspection of rail surface defect based on machine vision system, 2nd International Conference on Information Science and Engineering (ICISE), 3793 - 3796, 2010.
  • [10] Ying, L. Trinh, T. Haas, N. Otto, C. Pankanti, S. Rail Component Detection, Optimization, and Assessment for Automatic Rail Track Inspection, IEEE Transactions on Intelligent Transportation Systems, Vol. 15, 2014, 760 – 770.
  • [11] Dubey, A., Jaffery, Z. Maximally Stable Extremal Region Marking (MSERM) based Railway Track Surface Defect Sensing. IEEE Sensors Journal, 2016.
  • [12] Babenko, P. Visual inspection of railroad tracks (Doctoral dissertation, University of Central Florida Orlando, Florida), 2009.
  • [13] Li, Q. Shi J. Li, C. Fast line detection method for Railroad Switch Machine Monitoring System, In 2009 International Conference on Image Analysis and Signal Processing, pp. 61-64, 2009.
  • [14] Qi, Z., Tian, Y., Shi, Y. Efficient railway tracks detection and turnouts recognition method using HOG features. Neural Computing and Applications, 23(1), 245-254, 2013.
  • [15] Wang, P. Xu, J. Xie, K. Chen, R. Numerical simulation of rail profiles evolution in the switch panel of a railway turnout, Wear, 2016.
  • [16] Kassa, E. Nielsen, J.C. Dynamic interaction between train and railway turnout: full-scale field test and validation of simulation models, Vehicle System Dynamics, 46(S1), 521-534, 2008.
  • [17] Zwanenburg, W.J. Modelling degradation processes of switches & crossings for maintenance & renewal planning on the Swiss railway network, 2009.
  • [18] Jalili Hassankiadeh, S. Failure analysis of railway switches and crossings for the purpose of preventive maintenance, 2011.
  • [19] Schupp, G. Weidemann, C. Mauer, L. Modelling the contact between wheel and rail within multibody system simulation, Vehicle System Dynamics, 41(5), 349-364, 2004.
  • [20] Karakose M. Yaman, O. Akin E. Detection of Rail Switch Passages Through Image Processing on Railway Line and Use of Condition-Monitoring Approach, International Conference on Advanced Technology & Sciences (ICAT'16), pp. 99-105, Sept 2016.
  • [21] Yaman, O. Karakose, M. Akin, E. Aydin, I. Image processing based fault detection approach for rail surface, In Signal Processing and Communications Applications Conference (SIU), 2015, pp. 1118-1121.
  • [22] Qingyong, L. Shengwei, R. A Real-Time Visual Inspection System for Discrete Surface Defects of Rail Heads, IEEE Transactions on Instrumentation and Measurement, vol. 61, 2012, pp. 2189-2199.
  • [23] Bouchikhi, A. Boudraa, A.O. Cexus, J.C. Chonavel T., Analysis of multicomponent LFM signals by Teager Huang-Hough transform, IEEE Transactions on Aerospace and Electronic Systems, 1222-1233, 2014.
  • [24] Aydin, I. Karakose, E. Karakose, M. Gençoglu, M.T. Akın, E. A new computer vision approach for active pantograph control, In Innovations in Intelligent Systems and Applications (INISTA), 2013 IEEE International Symposium on (pp. 1-5). IEEE, 2013.
  • [25] Karakose, M. Sensor Based Intelligent Systems for Detection and Diagnosis, Journal of Sensors, 2016.
There are 25 citations in total.

Details

Subjects Engineering
Journal Section Research Article
Authors

Mehmet Karaköse

Orhan Yaman

Erhan Akın

Publication Date December 1, 2016
Published in Issue Year 2016 Special Issue (2016)

Cite

APA Karaköse, M., Yaman, O., & Akın, E. (2016). Real time implementation for fault diagnosis and condition monitoring approach using image processing in railway switches. International Journal of Applied Mathematics Electronics and Computers(Special Issue-1), 307-313. https://doi.org/10.18100/ijamec.270627
AMA Karaköse M, Yaman O, Akın E. Real time implementation for fault diagnosis and condition monitoring approach using image processing in railway switches. International Journal of Applied Mathematics Electronics and Computers. December 2016;(Special Issue-1):307-313. doi:10.18100/ijamec.270627
Chicago Karaköse, Mehmet, Orhan Yaman, and Erhan Akın. “Real Time Implementation for Fault Diagnosis and Condition Monitoring Approach Using Image Processing in Railway Switches”. International Journal of Applied Mathematics Electronics and Computers, no. Special Issue-1 (December 2016): 307-13. https://doi.org/10.18100/ijamec.270627.
EndNote Karaköse M, Yaman O, Akın E (December 1, 2016) Real time implementation for fault diagnosis and condition monitoring approach using image processing in railway switches. International Journal of Applied Mathematics Electronics and Computers Special Issue-1 307–313.
IEEE M. Karaköse, O. Yaman, and E. Akın, “Real time implementation for fault diagnosis and condition monitoring approach using image processing in railway switches”, International Journal of Applied Mathematics Electronics and Computers, no. Special Issue-1, pp. 307–313, December 2016, doi: 10.18100/ijamec.270627.
ISNAD Karaköse, Mehmet et al. “Real Time Implementation for Fault Diagnosis and Condition Monitoring Approach Using Image Processing in Railway Switches”. International Journal of Applied Mathematics Electronics and Computers Special Issue-1 (December 2016), 307-313. https://doi.org/10.18100/ijamec.270627.
JAMA Karaköse M, Yaman O, Akın E. Real time implementation for fault diagnosis and condition monitoring approach using image processing in railway switches. International Journal of Applied Mathematics Electronics and Computers. 2016;:307–313.
MLA Karaköse, Mehmet et al. “Real Time Implementation for Fault Diagnosis and Condition Monitoring Approach Using Image Processing in Railway Switches”. International Journal of Applied Mathematics Electronics and Computers, no. Special Issue-1, 2016, pp. 307-13, doi:10.18100/ijamec.270627.
Vancouver Karaköse M, Yaman O, Akın E. Real time implementation for fault diagnosis and condition monitoring approach using image processing in railway switches. International Journal of Applied Mathematics Electronics and Computers. 2016(Special Issue-1):307-13.

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