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| Self-supervised learning combined with adversarial transfer for cross-conditions bearing fault diagnosis |
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Abstract In the application of intelligent bearings fault diagnosis, it is extremely difficult to obtain sufficient real fault data due to the complexity and variability of the actual working conditions, and there exist large differences between the signals in the target and source domains, leading to the problems of difficult feature extraction and classification and weak generalization of the model in the cross-working condition transfer recognition of the deep model. Considering the existence of a large amount of unlabeled data in the target domain, this paper introduced unsupervised ideas and proposed an improved method based on self-supervised learning combined with adversarial transfer. Firstly, the pretext tasks are created based on the characteristics of the signal itself to learn from a large amount of unlabeled data and establish the intrinsic connections of fault categories between the source domain and the target domain; then the knowledge learned from the source domain is transferred to the target domain through adversarial domain adaptation and joint maximum mean difference, and finally, it combined with the pretexts task to optimize the difference between the two domains and achieved accurate fault classification in the target domain. Experimental results have verified the feasibility and good performance, which can achieve better diagnostic results and generalization of the model compared to other methods.
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Received: 15 February 2023
Published: 26 September 2024
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2024, Vol. 45 
