Bearing fault classification diagnosis based on group-sparsity learning and AVOA-XGBoost

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Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (18) : 96-105.

ZHANG Jixiang1，ZHANG Mengjian2，WANG Deguang1，YANG Ming1

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Abstract

In response to the challenge of low accuracy of bearing fault classification under strong background noises in industrial equipment, a bearing fault classification method based on group-sparsity learning and African vultures optimization algorithm-extreme gradient boosting (AVOA-XGBoost) is proposed. First, the bearing vibration signals are reconstructed using group-sparsity representation, which reduces the noise level and characterizes fault impulses more effectively. Then, time-domain, frequency-domain, and entropy features are extracted from the reconstructed signals and the feature set is constructed. Finally, the super parameters of XGBoost are adaptively adjusted by AVOA, which establishes a robust XGBoost for efficient bearing fault classification diagnosis. Experimental results demonstrate that the signals reconstructed by group-sparsity learning exhibit stronger fault characteristic representation, AVOA-XGBoost achieves higher classification accuracy compared with traditional machine learning models, and the proposed method can effectively diagnose the types and degrees of bearing faults.

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bearing fault diagnosis / group-sparsity learning / feature extraction / African vulture optimization algorithm / XGBoost

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ZHANG Jixiang1, ZHANG Mengjian2, WANG Deguang1, YANG Ming1. Bearing fault classification diagnosis based on group-sparsity learning and AVOA-XGBoost[J]. Journal of Vibration and Shock, 2024, 43(18): 96-105

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