Stress Detection of Railway Point Machine Using Sound Analysis


KIPS Transactions on Software and Data Engineering, Vol. 5, No. 9, pp. 433-440, Sep. 2016
10.3745/KTSDE.2016.5.9.433,   PDF Download:
Keywords: Railway Point Machine, Stress Detection, Sound Analysis
Abstract

Railway point machines act as actuators that provide different routes to trains by driving switchblades from the current position to the opposite one. Since point failure can significantly affect railway operations with potentially disastrous consequences, early stress detection of point machine is critical for monitoring and managing the condition of rail infrastructure. In this paper, we propose a stress detection method for point machine in railway condition monitoring systems using sound data. The system enables extracting sound feature vector subset from audio data with reduced feature dimensions using feature subset selection, and employs support vector machines (SVMs) for early detection of stress anomalies. Experimental results show that the system enables cost-effective detection of stress using a low-cost microphone, with accuracy exceeding 98％.


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Cite this article
[IEEE Style]
Y. Choi, J. Lee, D. Park, J. Lee, Y. Chung, H. Kim, S. Yoon, "Stress Detection of Railway Point Machine Using Sound Analysis," KIPS Transactions on Software and Data Engineering, vol. 5, no. 9, pp. 433-440, 2016. DOI: 10.3745/KTSDE.2016.5.9.433.

[ACM Style]
Yongju Choi, Jonguk Lee, Daihee Park, Jonghyun Lee, Yongwha Chung, Hee-Young Kim, and Sukhan Yoon. 2016. Stress Detection of Railway Point Machine Using Sound Analysis. KIPS Transactions on Software and Data Engineering, 5, 9, (2016), 433-440. DOI: 10.3745/KTSDE.2016.5.9.433.