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http://dx.doi.org/10.3745/KTSDE.2016.5.9.433

Stress Detection of Railway Point Machine Using Sound Analysis  

Choi, Yongju (고려대학교 컴퓨터정보학과)
Lee, Jonguk (고려대학교 컴퓨터정보학과)
Park, Daihee (고려대학교 컴퓨터정보학과)
Lee, Jonghyun ((주)세화)
Chung, Yongwha (고려대학교 컴퓨터정보학과)
Kim, Hee-Young (고려대학교 응용통계학과)
Yoon, Sukhan ((주)세화 부설연구소)
Publication Information
KIPS Transactions on Software and Data Engineering / v.5, no.9, 2016 , pp. 433-440 More about this Journal
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%.
Keywords
Railway Point Machine; Stress Detection; Sound Analysis; SVM;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
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