Deep prototypical networks based domain adaptation for fault diagnosis

doi:10.1007/s10845-020-01709-4

CORC > 自动化研究所 > 中国科学院自动化研究所 > 综合信息系统研究中心 > 工业智能技术与系统

	Deep prototypical networks based domain adaptation for fault diagnosis
	Wang, Huanjie 1,2; Bai, Xiwei 1,2; Tan, Jie 2; Yang, Jiechao 1,2
刊名	JOURNAL OF INTELLIGENT MANUFACTURING
	2020-11-11
页码	11
关键词	Bearing Fault diagnosis Domain adaptation Prototype learning
ISSN号	0956-5515
DOI	10.1007/s10845-020-01709-4
通讯作者	Tan, Jie(jie.tan@ia.ac.cn)
英文摘要	Due to the existence of domain shifts, the distributions of data acquired from different machines show significant discrepancies in industrial applications, which leads to performance degradation of traditional machine learning methods. In this paper, we propose a novel method that combines supervised domain adaptation with prototype learning for fault diagnosis. The proposed method consists of two modules, i.e., feature learning and condition recognition. The module of feature learning applies the Siamese architecture based on one-dimensional convolutional neural networks to learn a domain invariant subspace, which reduces the inter-domain discrepancy of distributions. The module of condition recognition applies a prototypical layer to learn the prototypes of each class. Then the classification task is simplified to find the nearest class prototype. Compared with existing intelligent fault diagnosis methods, this proposed method can be extended to address the problem when the classes from the source and target domains are partially overlapped. The model must generalize to unknown classes in the source domain, given only a few samples of each new target class. The effectiveness of the proposed method is verified using two bearing datasets. The model quickly converges with high classification accuracy using a few labeled target samples in training, even one per class can be effective.
资助项目	National Key Research and Development Program (CN)[2018YFB1703400] ; National Natural Science Foundation of China[U1801263] ; National Natural Science Foundation of China[U1701262]
WOS关键词	CLASSIFIER
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	SPRINGER
WOS记录号	WOS:000588582400001
资助机构	National Key Research and Development Program (CN) ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/41764]
专题	综合信息系统研究中心_工业智能技术与系统
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,Bai, Xiwei,Tan, Jie,et al. Deep prototypical networks based domain adaptation for fault diagnosis[J]. JOURNAL OF INTELLIGENT MANUFACTURING,2020:11.
APA	Wang, Huanjie,Bai, Xiwei,Tan, Jie,&Yang, Jiechao.(2020).Deep prototypical networks based domain adaptation for fault diagnosis.JOURNAL OF INTELLIGENT MANUFACTURING,11.
MLA	Wang, Huanjie,et al."Deep prototypical networks based domain adaptation for fault diagnosis".JOURNAL OF INTELLIGENT MANUFACTURING (2020):11.