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Parameter-driven acoustic fault diagnosis model for train bearings considering the Doppler effect

Published:2022-11-22 author:TENG Fan-rong, LIU Fang, ZHAI Zhong-ping, et al. Browse: 571 Check PDF documents

Parameter-driven acoustic fault diagnosis model for train

bearings considering the Doppler effect

TENG Fan-rong1, LIU Fang1, ZHAI Zhong-ping2,

HOU Chao-qiang1, ZHAI Tao-tao1, LIU Yong-bin1

(1.School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China;

2.Department of Precision Mechanics and Precision Instruments, University of

Science and Technology of China, Hefei 230027, China)

Abstract: Aiming at the problem that the accuracy of bearing fault diagnosis decreases due to the influence of Doppler effect on train bearing signals in trackside acoustic detection, an intelligent fault diagnosis method for train bearing acoustics based on parameter-driven learning model was proposed. Firstly, it was proposed to use a kinematic parameters drive security realm model(KPD-SRM) for diagnosis in the case of unbalanced samples in the early stage, and use a kinematic parameters drive convolutional neural networks (KPDCNN) for diagnosis in the case of sample balance in the later stage. Then, in the simulation case, the proposed method was used to diagnose the bearing samples of ten different fault types with unbalanced and balanced samples respectively, and the accuracy of fault diagnosis was calculated. Finally, under the experimental conditions, the proposed method was used to diagnose the faults of the bearing samples of four different fault types with the sample unbalanced and the sample balanced respectively, and the accuracy of fault diagnosis was calculated. The research results show that the diagnostic accuracy of the simulated case is 97.5% and 96%, respectively, and the diagnostic accuracy of the experimental case is 93.5% and 97%, respectively, in the two cases of sample imbalance and sample balance. The parameterdriven learning model can effectively use historical data to improve the diagnostic accuracy without complex signal correction, and it will continue to improve with the increase of monitoring samples.

Key words: wheel pair bearing; trackside acoustic detection system(TADS); signal correction; kinematic parameters drive security realm model(KPDSRM); kinematic parameters drive convolutional neural networks (KPD-CNN); accuracy rate of fault diagnosis;sample imbalance

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