The railway occupies a fairly important position in transportation due to its high speed and strong transportation capability. As a consequence, it is a key issue to guarantee continuous running and transportation safety of trains. Meanwhile, time consumption of the diagnosis procedure is of extreme importance for the detecting system. However, most of the current adopted techniques in the wayside acoustic defective bearing detector system (ADBD) are offline strategies, which means that the signal is analyzed after the sampling process. This would result in unavoidable time latency. Besides, the acquired acoustic signal would be corrupted by the Doppler effect because of high relative speed between the train and the data acquisition system (DAS). Thus, it is difficult to effectively diagnose the bearing defects immediately. In this paper, a new strategy called online Doppler effect elimination (ODEE) is proposed to remove the Doppler distortion online by the introduced unequal interval sampling scheme. The steps of proposed strategy are as follows: The essential parameters are acquired in advance. Then, the introduced unequal time interval sampling strategy is used to restore the Doppler distortion signal, and the amplitude of the signal is demodulated as well. Thus, the restored Doppler-free signal is obtained online. The proposed ODEE method has been employed in simulation analysis. Ultimately, the ODEE method is implemented in the embedded system for fault diagnosis of the train bearing. The results are in good accordance with the bearing defects, which verifies the good performance of the proposed strategy.
The wayside acoustic detector system is a potential technique in ensuring the safety of traveling vehicles. However, multisource aliasing and Doppler distortion in acquired acoustic signals decrease the accuracy of machine diagnosis. The conventional multisource separation schemes fail to solve the coaxial-moving sound source (CMSS) problem by constructing time-frequency filters and designing one-dimensional time-varying spatial filters. To address this issue, this paper combines spatial filtering with sparse filtering to solve this problem. Spatial filtering could suppress but not eliminate undesired sources. Sparse filtering has no capability of coping with non-stationary signals with Doppler distortion. The combination of spatial filtering and sparse filtering could make up their shortcomings and effectively solve the CMSS problem. The proposed scheme has two main advantages of eliminating residual interferences completely and suppressing background noise effectively. The simulation and experimental cases verify the effectiveness of the proposed method. The results indicate the potential of the proposed method to improve the performance of wayside acoustic detector systems.
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