基于多源域异构模型迁移的滚动轴承故障诊断方法

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Abstract For the problems of large distribution differences in rolling bearing vibration data under different specifications and working conditions in actual industrial production, and the resources of multiple similar data sets are not fully utilized, which lead to the low accuracy of diagnosis model, a rolling bearing fault diagnosis method based on heterogeneous model transfer in multi-source domain are proposed. Fourier transform is used to obtain the time-frequency spectra of the rolling bearing vibration signals. A variety of labeled data under different specifications and conditions are used as the multi-source domain, and a small amount of labeled data in other specifications and conditions are used as the target domain. The multiple source domains data are used to train ResNet-34 deep networks, the parameter transfer strategy of heterogeneous model is improved by using evolution strategies model agnostic meta learning, so that it can adaptively determine the level and content of knowledge transferred to the target domain. It is proposed to transfer the source domain knowledge to the VGG-16 deep networks to obtain multiple target domain models, the extracted features are input into the same extreme learning machine head to tail to achieve feature fusion and classification, and finally rolling bearing fault diagnosis model can be established. Experimental results show that the proposed method can achieve the transfer diagnosis between rolling bearings under different specifications and working conditions, and has a higher accuracy.

Key words： multi-source domain heterogeneous model meta learning rolling bearing fault diagnosis

Received: 17 November 2022 Published: 28 December 2023

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	Articles by authors
	WANG Yujing1
	XIA Lin1
	KANG Shouqiang1
	XIE Jinbao2
	WANG Qingyan1

Cite this article:

WANG Yujing1,XIA Lin1,KANG Shouqiang1, et al. A fault diagnosis method of rolling bearings based on multi-source domain heterogeneous model transfer[J]. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(24): 257-266.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2023/V42/I24/257

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