Research on filtering method of rolling bearing vibration signal based on improved Morlet wavelet

Yu Chen; Qingyang Meng; Zhibo Liu; Zhuanzhe Zhao; Yongming Liu; Zhijian Tu; Haoran Zhu; Yu Chen; Qingyang Meng; Zhibo Liu; Zhuanzhe Zhao; Yongming Liu; Zhijian Tu; Haoran Zhu

doi:10.3934/era.2024012

Electronic Research Archive

2024, Volume 32, Issue 1: 241-262. doi: 10.3934/era.2024012

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Research article

Research on filtering method of rolling bearing vibration signal based on improved Morlet wavelet

1.
School of Mechanical Engineering, Anhui Polytechnic University, Wuhu 241000, China
2.
Key Laboratory of Electric Drive and Control of Anhui Province, Anhui Polytechnic University, Wuhu 241000, China
3.
Anhui Provincial Key Laboratory of Discipline Co-construction on Intelligent Equipment Quality and Reliability, Wuhu 241000, China
4.
Center for Robot Performance Testing and Reliability Assessment, Anhui Polytechnic University, Wuhu 241000, China
5.
School of Artificial Intelligence, Anhui Polytechnic University, Wuhu 241000, China
6.
Wuhu Ceprei Robotics Industry Technology Research Institute Co. Ltd., Wuhu 241003, China

Received: 14 November 2023 Revised: 04 December 2023 Accepted: 12 December 2023 Published: 21 December 2023

In response to the challenge of noise filtering for the impulsive vibration signals of rolling bearings, this paper presented a novel filtering method based on the improved Morlet wavelet, which has clear physical meaning and is more conducive to parameter optimization through employing Gaussian waveform width to replace the traditional Morlet wavelet shape factor. Simultaneously, the marine predation algorithm was employed and the minimum Shannon entropy was used as the parameter optimization index while optimizing the shape width and center frequency of the improved Morlet wavelet. The vibration waveform of the rolling bearing was matched perfectly by using the optimized Morlet wave. Shannon entropy was used as the evaluation index of noise filtering, and the quantitative analysis of noise filtering was realized. Through experimental validation, this method was proved to be effective in noise elimination for rolling bearing. It is significance to preprocessing of vibration signal, feature extraction and fault recognition of rolling bearing.
- Morlet wavelet,
- rolling bearings,
- waveform matching,
- noise reduction filtering
Citation: Yu Chen, Qingyang Meng, Zhibo Liu, Zhuanzhe Zhao, Yongming Liu, Zhijian Tu, Haoran Zhu. Research on filtering method of rolling bearing vibration signal based on improved Morlet wavelet[J]. Electronic Research Archive, 2024, 32(1): 241-262. doi: 10.3934/era.2024012

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Abstract

In response to the challenge of noise filtering for the impulsive vibration signals of rolling bearings, this paper presented a novel filtering method based on the improved Morlet wavelet, which has clear physical meaning and is more conducive to parameter optimization through employing Gaussian waveform width to replace the traditional Morlet wavelet shape factor. Simultaneously, the marine predation algorithm was employed and the minimum Shannon entropy was used as the parameter optimization index while optimizing the shape width and center frequency of the improved Morlet wavelet. The vibration waveform of the rolling bearing was matched perfectly by using the optimized Morlet wave. Shannon entropy was used as the evaluation index of noise filtering, and the quantitative analysis of noise filtering was realized. Through experimental validation, this method was proved to be effective in noise elimination for rolling bearing. It is significance to preprocessing of vibration signal, feature extraction and fault recognition of rolling bearing.

References

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