Abstract
Signalling systems play a major role in railway reliability. However, microcomputer-based monitoring system (MMS), which monitors signal currents, simply raises an alarm when a signal has a failure but cannot recognize the exact reason of the failure. Therefore, we propose an intelligent diagnosis approach to help MMS to recognize faults automatically. First, the approach divides signal current curves collected by MMS into numerous sections with a same length of 600 s. Second, it utilizes dynamic time warping (DTW) to calculate similarities between reference curves and the 600 s-long curves and identify normal ones and a certain type of fault ones named as fluctuant curves. Third, our approach adopts three rules to further distinguish the rest into three types of fault curves. Finally, we conduct an experiment, and the results indicate that our approach can automatically diagnose signal fault curves with 100% accuracy.
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Acknowledgements
This research is supported by Sichuan science and technology program (2019YFG0040) and the National Key R&D Program of China (2016YFB1200402) and the National Natural Science Foundation of China (Grant No. 61703308). The authors gratefully acknowledge the invaluable contribution of the reviewers.
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Huang, S., Wu, Z., Zhang, F., Yu, K., Yang, L. (2020). Recognition of Signal Fault Curves Based on Dynamic Time Warping for Rail Transportation. In: Qin, Y., Jia, L., Liu, B., Liu, Z., Diao, L., An, M. (eds) Proceedings of the 4th International Conference on Electrical and Information Technologies for Rail Transportation (EITRT) 2019. EITRT 2019. Lecture Notes in Electrical Engineering, vol 639. Springer, Singapore. https://doi.org/10.1007/978-981-15-2866-8_18
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DOI: https://doi.org/10.1007/978-981-15-2866-8_18
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