Research on bearing fault diagnosis method based on improved support vector machine

Zemin Liu; Gaohua Chen; Xiaoxia Gao

doi:10.1117/12.3039398

4 September 2024 Research on bearing fault diagnosis method based on improved support vector machine

Zemin Liu, Gaohua Chen, Xiaoxia Gao

Proceedings Volume 13259, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2024); 132590A (2024) https://doi.org/10.1117/12.3039398
Event: Fourth International Conference on Automation Control, Algorithm, and Intelligent Bionics (ICAIB 2024), 2024, Yinchuan, China

Abstract

Aiming at the current fault characteristics of rolling bearing vibration signals with non-linear and non-smooth characteristics that are not clear, a rolling bearing fault diagnosis method based on improved support vector machine (SVM) is proposed. The Improved Whale Algorithm (IWOA) is used to optimize the support vector machine model parameters in order to create the best IWOA-SVM identifying faults model for motor bearing defect classification. According to the results of the study, the use of the IWOA-SVM classification model demonstrated significant results in fault diagnosis with an accuracy of 99.33%.

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

Citation Download Citation

Zemin Liu, Gaohua Chen, and Xiaoxia Gao "Research on bearing fault diagnosis method based on improved support vector machine", Proc. SPIE 13259, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2024), 132590A (4 September 2024); https://doi.org/10.1117/12.3039398

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