A Two-Stage Learning Model for Track-Side Acoustic Bearing Fault Diagnosis

Abstract

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 the sides of the railway. The signal distortion caused by the Doppler effect is a barrier to 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 parameter-driven learning model named kinematic-parameter-driven safety region model (KPD-SRM)/kinematic-parameter-driven backpropagation neural network (KPD-BPNN) is proposed, which provides a novel way for track-side acoustic fault diagnosis and it has the following merits. First, this is a breakthrough to the existing methods based on signal correction, the diagnosis decision does not require signal correction as a prerequisite. Second, 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. Finally, 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.