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Sensors 2016, 16(4), 549; doi:10.3390/s16040549

Fault Detection and Diagnosis of Railway Point Machines by Sound Analysis

1
Department of Computer and Information Science, Korea University, Sejong Campus, Sejong City 30019, Korea
2
Department of Applied Statistics, Korea University, Sejong Campus, Sejong City 30019, Korea
3
Sehwa R&D Center, Techno 2-ro, Yuseong-gu, Daejeon 34026, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 12 February 2016 / Revised: 12 April 2016 / Accepted: 12 April 2016 / Published: 16 April 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [6002 KB, uploaded 16 April 2016]   |  

Abstract

Railway point devices act as actuators that provide different routes to trains by driving switchblades from the current position to the opposite one. Point failure can significantly affect railway operations, with potentially disastrous consequences. Therefore, early detection of anomalies is critical for monitoring and managing the condition of rail infrastructure. We present a data mining solution that utilizes audio data to efficiently detect and diagnose faults in railway condition monitoring systems. The system enables extracting mel-frequency cepstrum coefficients (MFCCs) from audio data with reduced feature dimensions using attribute subset selection, and employs support vector machines (SVMs) for early detection and classification of anomalies. Experimental results show that the system enables cost-effective detection and diagnosis of faults using a cheap microphone, with accuracy exceeding 94.1% whether used alone or in combination with other known methods. View Full-Text
Keywords: railway point machine; railway condition monitoring system; audio data; support vector machine railway point machine; railway condition monitoring system; audio data; support vector machine
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Lee, J.; Choi, H.; Park, D.; Chung, Y.; Kim, H.-Y.; Yoon, S. Fault Detection and Diagnosis of Railway Point Machines by Sound Analysis. Sensors 2016, 16, 549.

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