Wayside acoustic fault diagnosis of train wheel bearing based on Doppler effect correction and fault-relevant information enhancement

Health monitoring of train bearing is crucial to railway transport safety. More and more attention has elicited by the wayside acoustic monitoring technique in recent years than other defect detection techniques. However, wayside acoustic signal contains serious Doppler distortion and heavy background noise because of the high speed of trains. Thus, extracting fault-relevant information is difficult. A novel method for Doppler effect correction is proposed in this study by incorporating the traditional time-domain interpolation resampling with a novel kinematic parameters estimation method. In this kinematic parameters estimation method, an iterative algorithm based on least squares theory is proposed to improve the parameters estimation accuracy. After the Doppler effect correction, the ensemble empirical mode decomposition is employed to further enhance the fault-relevant information. The proposed iteration algorithm can improve the accuracy of kinematic parameters estimation significantly; thus the Doppler distortion can be corrected more accurately. The proposed ensemble empirical mode decomposition can further enhance the fault-relevant information and so that the accuracy and reliability of the diagnosis decision can be improved. The performance of this method has been verified in experimental and simulated cases.