Stress Detection of Railway Point Machine Using Sound Analysis

KIPS Transactions on Software and Data Engineering, Vol. 5, No.9, pp.433-440, September 2016
10.3745/KTSDE.2016.5.9.433, Full Text

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%.


Statistics

Show / Hide Statistics

Statistics (Cumulative Counts from October 15, 2016)

Multiple requests among the same browser session are counted as one view. If you mouse over a chart, the values of data points will be shown.


Cite this paper

[KIPS Transactions Style]
Y. Choi, J. Lee, D. Park, J. Lee, Y. Chung, H. Kim, and 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.

[IEEE Style]
Yongju Choi, Jonguk Lee, Daihee Park, Jonghyun Lee, Yongwha Chung, Hee-Young Kim, and Sukhan 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]
Choi, Y., Lee, J., Park, D., Lee, J., Chung, Y., Kim, H., and Yoon, S. 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.