深度领域自适应及其在跨工况故障诊断中的应用

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Abstract In the real-world production, variable working conditions lead to the distribution discrepancy in the monitoring data, which undermines the basis of classification models and consequently deteriorates the diagnosis accuracy. Therefore, a deep learning-based domain adaptation method is proposed for rolling bearing fault diagnosis across working conditions. In this method, two deep networks are constructed in a cascaded way. The first one is applied to process vibration signals and automatically mine fault-sensitive features. The second one maps sample features from different working conditions onto a deep hidden layer (the common feature space) to reduce the distribution discrepancy caused by fluctuant working conditions, generate working-condition-independent features, and realize domain adaptation. Moreover, this deep projection network is adaptively established by parameter optimization for the best cross-domain diagnosis performance. The experiments show that, compared with peer approaches and related literatures, deep domain adaptation has a higher accuracy in fault recognition across working conditions.

Key words： deep learning domain adaptation variable working condition fault diagnosis

Received: 10 January 2019 Published: 15 June 2020

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	Articles by authors
	YUAN Zhuang1
	2
	DONG Rui2
	ZHANG Laibin1
	DUAN Lixiang1

Cite this article:

YUAN Zhuang1,2,DONG Rui2, et al. Deep domain adaptation and its application in fault diagnosis across working conditions[J]. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(12): 281-288.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2020/V39/I12/281

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