A multi-source unsupervised domain adaptive bearing fault diagnosis method integrating time-frequency features

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Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (13) : 12-24.

JIN Huaiping1,2, LIU Zhiyong1,2, WANG Bin1,2, QIAN Bin1,2, LIU Haipeng1,2

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Abstract

Unsupervised domain adaptation methods have become an important approach for bearing fault diagnosis under multiple operating conditions. However, existing multi-source unsupervised domain adaptation methods often ignore the contribution of signals from different perspectives to cross-domain fault diagnosis, thus failing to comprehensively represent the fault characteristics of bearings. Additionally, these methods often encounter discrepancies of the prediction results from different source domains for the same target domain task. To address these issues, a time-frequency features fused multi-source unsupervised domain adaptation (TFFMUDA) method is proposed for bearing fault diagnosis. TFFMUDA takes both time-domain and frequency-domain signals as inputs, which interact through a feature coupling mechanism. Meanwhile, the diagnostic consistency of different source domains for the same target domain is guaranteed through classifier alignment strategy. Experimental results on a real bearing fault case demonstrate that the proposed method achieves clearer decision boundaries for fault classes and exhibits improved accuracy for bearing fault diagnosis compared to existing domain adaptation methods.

Key words

bearing fault diagnosis / multi-source unsupervised domain adaptation / time-frequency features / feature fusion / feature coupling

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JIN Huaiping1,2, LIU Zhiyong1,2, WANG Bin1,2, QIAN Bin1,2, LIU Haipeng1,2. A multi-source unsupervised domain adaptive bearing fault diagnosis method integrating time-frequency features[J]. Journal of Vibration and Shock, 2024, 43(13): 12-24

References

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