Sparse-representation-based signal separation for high-speed train bearing fault diagnosis

Fang Liu; Zixiang Liu; Yifan Xu; Zihao Zhu; Yongbin Liu

doi:10.1117/12.3005785

10 October 2023 Sparse-representation-based signal separation for high-speed train bearing fault diagnosis

Fang Liu, Zixiang Liu, Yifan Xu, Zihao Zhu, Yongbin Liu

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 127992V (2023) https://doi.org/10.1117/12.3005785
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

A moving sound signal separation method based on the sparse representation of Doppler atoms is proposed to address the interference problem of wheel rail contact noise in the process of collecting train bearing signals. Using a single microphone to collect sound signals, first Doppler modulation is performed on dictionary atoms; Then perform sparse matching decomposition on the acoustic signal to obtain the sparse signal; Finally, the signal reconstruction theory is used to separate the sound source and obtain the bearing signal. Through experimental signals, it has been proven that under the influence of Doppler effect, the proposed method can effectively extract the target sound source and has good in band noise cancellation effect.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Fang Liu, Zixiang Liu, Yifan Xu, Zihao Zhu, and Yongbin Liu "Sparse-representation-based signal separation for high-speed train bearing fault diagnosis", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 127992V (10 October 2023); https://doi.org/10.1117/12.3005785

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