Rolling bearing fault diagnosis based on multiple wavelet feature fusion method

Yuntao Li; Hanyu Zhang; Yanan Jiang; Zitong Zhang

doi:10.1117/12.3015587

15 March 2024 Rolling bearing fault diagnosis based on multiple wavelet feature fusion method

Yuntao Li, Hanyu Zhang, Yanan Jiang, Zitong Zhang

Proceedings Volume 13079, Third International Conference on Testing Technology and Automation Engineering (TTAE 2023); 1307914 (2024) https://doi.org/10.1117/12.3015587
Event: 3rd International Conference of Testing Technology and Automation Engineering (TTAE 2023), 2023, Xi-an, China

Abstract

A feature extraction method called MWBF, based on the fusion of multiple wavelet bases, is proposed to address the issue of insufficient feature representation when applying a single wavelet base for wavelet packet transform on non-stationary rolling bearing signals. We constructed an initial feature vector by performing a multi-wavelet packet decomposition on the signal and extracting statistical characteristics. Subsequently, we optimized the feature selection using the Linear Discriminant Analysis (LDA) method. Finally, different machine learning models are employed for the classification of multiple fault modes in rolling bearings such as k-nearest neighbor (kNN) and logistic regression (LR). Experimental results on the XJTU-SY bearing dataset show that the diagnostic accuracy of the model using the MWBF method (99.75% ~ 99.83%) is significantly higher than that of the wavelet packet feature extraction method based on a single wavelet basis (95.22% ~ 97.46%). The MWBF-LR model achieved a classification accuracy of 99.83% when using LR as the subsequent classifier.

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

Citation Download Citation

Yuntao Li, Hanyu Zhang, Yanan Jiang, and Zitong Zhang "Rolling bearing fault diagnosis based on multiple wavelet feature fusion method", Proc. SPIE 13079, Third International Conference on Testing Technology and Automation Engineering (TTAE 2023), 1307914 (15 March 2024); https://doi.org/10.1117/12.3015587

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