Label propagation-based unsupervised domain adaptation for intelligent fault diagnosis

doi:10.1007/s10845-023-02186-1

CORC > 自动化研究所 > 中国科学院自动化研究所 > 中科院工业视觉智能装备工程实验室

	Label propagation-based unsupervised domain adaptation for intelligent fault diagnosis
	Wang, Huanjie 1,2; Li, Yuan 1,2; Bai, Xiwei 2; Li, Jingwei 1,2; Tan, Jie 1,2; Liu, Chengbao 2
刊名	JOURNAL OF INTELLIGENT MANUFACTURING
	2023-08-23
页码	18
关键词	Intelligent diagnosis Rotating machinery Label propagation Subpopulation shift Unsupervised domain adaptation
ISSN号	0956-5515
DOI	10.1007/s10845-023-02186-1
通讯作者	Tan, Jie(jie.tan@ia.ac.cn)
英文摘要	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.
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]
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	SPRINGER
WOS记录号	WOS:001053541000001
资助机构	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
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/54116]
专题	中科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.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
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.