Doppler distortion removal based on Dopplerlet transform and re-sampling for wayside fault diagnosis of train bearings

In wayside fault diagnosis of train bearings, the phenomenon of Doppler distortion in the acoustic signal of moving acoustic source acquired with a microphone leads to the difficulty for signal analysis. In this paper, a new method based on Dopplerlet transform and re-sampling is proposed to remove the Doppler distortion, and applied in the fault diagnosis of train bearings. Firstly, search the parameters space to find the primary functions-Dopplerlet atoms. According to the Morse acoustic theory and Doppler effect, the instantaneous frequency of the Dopplerlet atom which we choose to remove Doppler distortion of the corresponding acoustic source can be acquired. Then, the re-sampling sequence can be established as the re-sampling vector in time domain. Through the resample, the Doppler distortion effect can be removed. Finally, simulations and experiments with practical acoustic signals of train bearings with a defect on the outer race and the inner race are carried out, and the results verified the effectiveness of this method. Comparing with the other methods of Doppler distortion removal, this method works without measuring the motion parameters in advance, and is quite robust to noise. Meanwhile, this method has the potential to eliminate the Doppler distortion of original signal with multiple sources.

Doppler effect reduction scheme via acceleration-based Dopplerlet transform and resampling method for the wayside acoustic defective bearing detector system

In a wayside acoustic defective bearing detector system, bearing faults are detected through the analysis of the acoustic signal generated by the bearings of a passing vehicle. As vehicles pass by with high speeds, the acoustic signal recorded by the stationary microphone is disturbed by the Doppler effect. The reduction of the frequential structure disturbance of signals facilitates the efficient diagnosis of bearing faults. This study proposes a Doppler effect reduction scheme for the removal of the frequential structure disturbances of Doppler-shifted signals in the acoustic defective bearing detector system. First, the parameters, including the initial speed and the initial acceleration of the vehicle, are estimated by the acceleration-based Dopplerlet transform via the matching pursuit algorithm. Second, the time vector for resampling is calculated according to the estimated initial speed, the initial acceleration in the first step, the sound speed, and the measured geometric parameters of the ADBD system. Finally, the distorted signal is resampled through spline interpolation. Simulation and experimental cases are used to validate the effectiveness of the proposed scheme. Compared with the steady-motion-based method, the proposed scheme can better capture the true time-varying nature of Doppler-shifted signals. Moreover, this scheme is also robust to noise.

Doppler Distortion Removal Method for Multiple Acoustic Sources

In wayside fault diagnosis of train bearings, the phenomenon of Doppler distortion in the acoustic signal of moving acoustic source acquired with a microphone leads to the difficulty for signal analysis. In this paper, a new method based on Dopplerlet transform and re-sampling is proposed to eliminate the Doppler distortion of multiple acoustic sources which provide a reference for wayside fault diagnosis of train bearings. Firstly, search the parameters space to find the primary functionsDopplerlet atoms. According to the Morse acoustic theory, the instantaneous frequency of the Dopplerlet atom which we choose to remove Doppler distortion of the corresponding acoustic source can be acquired. Then, the re-sampling sequence can be established in time domain. Through the resample, the Doppler distortion can be removed. In the end of this paper, an experiment with practical acoustic signals is carried out, and the results verified the effectiveness of this method.