TY  - JOUR
PY  - 2016//
TI  - Fault detection and diagnosis of railway point machines by sound analysis
JO  - Sensors (Basel)
A1  - Lee, Jonguk
A1  - Choi, Heesu
A1  - Park, Daihee
A1  - Chung, Yongwha
A1  - Kim, Hee-Young
A1  - Yoon, Sukhan
SP  - e16040549
EP  - e16040549
VL  - 16
IS  - 4
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 1424-8220
UR  - http://dx.doi.org/10.3390/s16040549
ID  - ref1
ER  -