Fault diagnosis of rolling bearing based on CS-fuzzy neural network and wavelet packet transform

Detang Wang; Houzhi Zhang; Yueshuai Cao; Bo Dong

doi:10.1117/12.2625719

12 December 2021 Fault diagnosis of rolling bearing based on CS-fuzzy neural network and wavelet packet transform

Detang Wang, Houzhi Zhang, Yueshuai Cao, Bo Dong

Proceedings Volume 12127, International Conference on Intelligent Equipment and Special Robots (ICIESR 2021); 121270H (2021) https://doi.org/10.1117/12.2625719
Event: International Conference on Intelligent Equipment and Special Robots (ICIESR 2021), 2021, Qingdao, China

Abstract

Aiming at the problem of low accuracy in fault diagnosis of mechanical bearing in coal mine. A method based on CS-fuzzy neural network and wavelet packet transform is proposed for bearing fault diagnosis. Firstly, the collected data is processed by wavelet packet decomposition and reconstruction, and the fault feature vector is established. Then, the CS-fuzzy neural network based on improved cuckoo search algorithm is used to identify and diagnose the bearing fault type. Through experimental verification, the combination method of wavelet packet decomposition and cs-fuzzy neural network can diagnose bearing fault more accurately and quickly.

Citation Download Citation

Detang Wang, Houzhi Zhang, Yueshuai Cao, and Bo Dong "Fault diagnosis of rolling bearing based on CS-fuzzy neural network and wavelet packet transform", Proc. SPIE 12127, International Conference on Intelligent Equipment and Special Robots (ICIESR 2021), 121270H (12 December 2021); https://doi.org/10.1117/12.2625719

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