Fault diagnosis method for rolling bearings based on adaptive dynamic mode decomposition

Ying Huang; Renhu Ye; Rui Wang

doi:10.1117/12.3051704

18 November 2024 Fault diagnosis method for rolling bearings based on adaptive dynamic mode decomposition

Ying Huang, Renhu Ye, Rui Wang

Author Affiliations +

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134033V (2024) https://doi.org/10.1117/12.3051704
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

In response to the vibration signals of rolling bearings containing strong background noise, this paper uses multi-scale fuzzy entropy as the threshold for the dynamic mode decomposition mode to effectively distinguish low rank feature information from sparse noise components in the original signal, thereby reducing the impact of noise on fault extraction. At the same time, the IPSO algorithm is used to adaptively select thresholds and truncation ranks in dynamic mode decomposition, avoiding the shortcomings of traditional truncation rank hard thresholds that cannot effectively select truncation ranks, and achieving effective processing of fault signals. The fault feature frequency is successfully extracted, and the fault diagnosis of rolling bearings is achieved.

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

Citation Download Citation

Ying Huang, Renhu Ye, and Rui Wang "Fault diagnosis method for rolling bearings based on adaptive dynamic mode decomposition", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134033V (18 November 2024); https://doi.org/10.1117/12.3051704

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