基于多源域自适应残差网络的滚动轴承故障诊断

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Abstract Aiming at the weak applicability of the traditional unsupervised domain adaptive method to multi-condition rolling bearing fault diagnosis scenarios, a Multi-source Domain Adaptive Residual Network (MDARN) was proposed. By aligning Correlated subdomains from multiple source domains, thus improving the fault diagnosis performance of the model under multiple operating conditions. First, the ResNeXt residual network is used to fully extract transferable features from the source domain and the target domain; then, the local maximum mean difference (LMMD) criterion is introduced to align the relevant subdomains in the target domain based on the subdomains of the two source domains, reducing The distribution difference between the relevant sub-domains and the global domain; finally, the proposed method is verified experimentally by using the bearing data set of Case Western Reserve University and the real bearing vibration data set generated by the MFS mechanical comprehensive fault test bench. The results show that the average fault diagnosis accuracy of this method is as high as 99.76% under multiple working conditions. Compared with the existing representative methods, the proposed method has better fault diagnosis effect.

Key words： fault diagnosis of rolling bearing Multi-source Domain Adaption Residual Network domain adaptation local maximum mean discrepancy

Received: 19 April 2023 Published: 15 April 2024

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	Articles by authors
	GAO Xuejin1
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	ZHANG Zhenhua1
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	GAO Huihui1
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	QI Yongsheng5

Cite this article:

GAO Xuejin1,2,3, et al. Rolling bearing fault diagnosis based on multi-source domain adaptive residual network[J]. JOURNAL OF VIBRATION AND SHOCK, 2024, 43(7): 290-299.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2024/V43/I7/290

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