Label propagation-based unsupervised domain adaptation for intelligent fault diagnosis

doi:10.1007/s10845-023-02186-1

	Label propagation-based unsupervised domain adaptation for intelligent fault diagnosis
	Wang, Huanjie1,2 ; Li, Yuan 1,2; Bai, Xiwei1 ; Li, Jingwei1,2 ; Tan, Jie1,2 ; Liu, Chengbao1
发表期刊	JOURNAL OF INTELLIGENT MANUFACTURING
ISSN	0956-5515
	2023-08-23
页码	18
通讯作者	Tan, Jie(jie.tan@ia.ac.cn)
摘要	Current unsupervised domain adaptation methods for intelligent fault diagnosis focus on learning domain-invariant representations under covariate shift assumption. However, the covariate shift assumption is usually unsatisfied when each fault class in different domains consists of multiple modes with skewed proportions, which is common in industrial scenarios. Imbalanced data from multiple modes cause the presence of within-domain class imbalance and between-domain label distributional shift. This paper introduces a novel subpopulation shift that further considers the domain shift from a subpopulation perspective, i.e., that covariate shift and label distributional shift across domains are caused by shifts in the multiple modes. To address this issue, a label propagation-based unsupervised domain adaptation is proposed based on a realistic expansion assumption. We apply the theoretical analysis of the proposed method with a bi-level optimization strategy adapted from meta-learning. Using joint optimization of a teacher model and a student model, the label propagation-based model-agnostic meta-learning (LPMAML) not only propagates supervision information from the source to the target but also adjusts the teacher's strategy throughout the student's learning process. To alleviate the noise caused by label distributional shift, we integrate a sampling-based alignment method that aligns the empirical label distributions across the two domains into LPMAML. Experimental results on three bearing datasets show that the proposed method has impressive generalization ability under covariate, label distributional, and subpopulation shifts. The proposed method offers consistent improvements to unsupervised domain adaptation (UDA) methods. Compared with the vanilla UDA methods, the average diagnosis accuracies of the proposed method on the label distributional shift benchmark and subpopulation shift benchmark are improved by 8.21% and 7.63%, respectively.
关键词	Intelligent diagnosis Rotating machinery Label propagation Subpopulation shift Unsupervised domain adaptation
DOI	10.1007/s10845-023-02186-1
收录类别	SCI
语种	英语
资助项目	This work was supported in part by the National Key Research and Development Program of China under Grant 2022YFB3304602 and the National Nature Science Foundation of China under Grant 62003344.[62003344] ; National Key Research and Development Program of China ; National Nature Science Foundation of China ; [2022YFB3304602]
项目资助者	This work was supported in part by the National Key Research and Development Program of China under Grant 2022YFB3304602 and the National Nature Science Foundation of China under Grant 62003344. ; National Key Research and Development Program of China ; National Nature Science Foundation of China
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Manufacturing
WOS记录号	WOS:001053541000001
出版者	SPRINGER
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/54117
专题	中国科学院工业视觉智能装备工程实验室
通讯作者	Tan, Jie
作者单位	1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Wang, Huanjie,Li, Yuan,Bai, Xiwei,et al. Label propagation-based unsupervised domain adaptation for intelligent fault diagnosis[J]. JOURNAL OF INTELLIGENT MANUFACTURING,2023:18.
APA	Wang, Huanjie,Li, Yuan,Bai, Xiwei,Li, Jingwei,Tan, Jie,&Liu, Chengbao.(2023).Label propagation-based unsupervised domain adaptation for intelligent fault diagnosis.JOURNAL OF INTELLIGENT MANUFACTURING,18.
MLA	Wang, Huanjie,et al."Label propagation-based unsupervised domain adaptation for intelligent fault diagnosis".JOURNAL OF INTELLIGENT MANUFACTURING (2023):18.