Bearing condition monitoring and fault diagnosis based on parabolic acoustic mirror and blind source separation

Xin Huang; Fang Liu; Siwei Huang; Zehua Li; Hongqing Chen; Ziyu Yuan; Yongbin Liu

doi:10.1117/12.3055398

12 December 2024 Bearing condition monitoring and fault diagnosis based on parabolic acoustic mirror and blind source separation

Xin Huang, Fang Liu, Siwei Huang, Zehua Li, Hongqing Chen, Ziyu Yuan, Yongbin Liu

Author Affiliations +

Proceedings Volume 13439, Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024); 134390U (2024) https://doi.org/10.1117/12.3055398
Event: Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024), 2024, Xiamen, China

Abstract

This article proposes a new method for bearing acoustic detection based on a parabolic acoustic mirror. The parabolic acoustic mirror is used instead of the traditional bearing end cover, and a directional microphone is placed at the focal point of the parabolic acoustic mirror. The fault sound emitted by the bearing is focused on the directional microphone after being reflected by the parabolic acoustic mirror, achieving signal gain acquisition. Due to the inherent properties of paraboloids, non-parallel incident sound waves cannot converge at the focal point, thus achieving directional acquisition of bearing fault signals. Further, the single channel underdetermined blind source separation algorithm is used to extract the sound signal emitted by the bearing fault point, achieving high-quality acquisition of fault signals. Through experimental comparison and analysis with vibration signals and microphone direct sampling signals, it has been proven that the bearing fault signals collected by the parabolic acoustic mirror have certain advantages compared to the other two methods, indicating the feasibility of collecting bearing fault signals by the parabolic acoustic mirror.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xin Huang, Fang Liu, Siwei Huang, Zehua Li, Hongqing Chen, Ziyu Yuan, and Yongbin Liu "Bearing condition monitoring and fault diagnosis based on parabolic acoustic mirror and blind source separation", Proc. SPIE 13439, Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024), 134390U (12 December 2024); https://doi.org/10.1117/12.3055398

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