Fault Detection and Diagnosis of Railway Point Machines by Sound Analysis.
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.
Department of Computer and Information Science, Korea University, Sejong Campus, Sejong City 30019, Korea. eastwest9@korea.ac.kr.
Jonguk
J
Lee
Department of Computer and Information Science, Korea University, Sejong Campus, Sejong City 30019, Korea. chlgmltn420@korea.ac.kr.
Heesu
H
Choi
Department of Computer and Information Science, Korea University, Sejong Campus, Sejong City 30019, Korea. dhpark@korea.ac.kr.
Daihee
D
Park
Department of Computer and Information Science, Korea University, Sejong Campus, Sejong City 30019, Korea. ychungy@korea.ac.kr.
Yongwha
Y
Chung
Department of Applied Statistics, Korea University, Sejong Campus, Sejong City 30019, Korea. starkim@korea.ac.kr.
Hee-Young
HY
Kim
Sehwa R&amp;D Center, Techno 2-ro, Yuseong-gu, Daejeon 34026, Korea. shy5406@sehwa.biz.
Sukhan
S
Yoon
audio data; railway condition monitoring system; railway point machine; support vector machine