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

Abstract

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.

WITHDRAWN: Condition Monitoring of Metro Wheelsets Using Wayside Vibration and Acoustic Sensors

“…Plenty of efforts; time domain and frequency energy statistics [24], using low pass filtering and Title root mean square of the signal with appropriate time windows and applying Fourier transform afterwards [25] have been given but argued to be less efficient than vibration signals [26]. However, acoustic wayside application is reported to be inefficient when Doppler correction is employed before STFT -Short Time Fourier Transform like approaches [27]. Vibration sensors (accelerometers) can also be used in the diagnosis of wheel defects efficiently.…”

A Survey on Fault Diagnosis Approaches for Rolling Bearings of Railway Vehicles

“…Innovative methods integrating microphone arrays and matching tracking algorithms also help to achieve similar results [122]. Liu et al combined traditional time-domain interpolation with kinematic parameter estimation and proposed an iterative algorithm based on least-squares theory [123]. They indicated that the interval between the microphone array and the middle of the track should be maintained steady to reduce the amount of calculation of additional parameters.…”

Study on the Extraction Method for Track-Side Acoustic Features Based on Cyclic Stationary Analysis

“…The resampled time series is obtained by re-interpolating the faulty sound source's time series T i received by the acquisition device [29]. Interpolating and resampling a Doppler-distorted signal involves correcting the nonlinear variation in the acquired acoustic signal's time series to a linear variation.…”