A Two-stage Learning Model for Track-side Acoustic Bearing Fault Diagnosis

The idea of track-side acoustic detection technology is to extract fault-related information from the sound signal emitted by train bearings collected by microphones installed on sides of the railway. The signal distortion caused by the Doppler Effect is a barrier for efficient fault diagnosis. Currently, signal correction is the main way to solve this problem. Alternatively, this study attempts to directly construct the functional relationship between the Doppler-shifted signal and the diagnosis decision. Specifically, a two-stage parameters-driven learning model named KPD-SRM/KPD-BPNN is proposed, which provides a novel way for track-side acoustic fault diagnosis and it has the following merits: (1) This is a breakthrough to the existing methods based on signal correction, the diagnosis decision does not require signal correction as a prerequisite; (2) With the employment of machine learning methods, historical data can be used to improve the diagnostic accuracy and it will be continuously improved along with the increase in monitoring samples; (3) The proposed two-stage learning model can solve the problem of sample imbalance, so it has a good prospect of practical engineering application. Both simulation and experimental analysis prove the effectiveness of the proposed method.