Knowledge Commons of Institute of Automation,CAS
Deep prototypical networks based domain adaptation for fault diagnosis | |
Wang, Huanjie1,2; Bai, Xiwei1,2; Tan, Jie1; Yang, Jiechao1,2 | |
发表期刊 | JOURNAL OF INTELLIGENT MANUFACTURING |
ISSN | 0956-5515 |
2020-11-11 | |
页码 | 11 |
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. |
关键词 | Bearing Fault diagnosis Domain adaptation Prototype learning |
DOI | 10.1007/s10845-020-01709-4 |
关键词[WOS] | CLASSIFIER |
收录类别 | SCI |
语种 | 英语 |
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] |
项目资助者 | National Key Research and Development Program (CN) ; National Natural 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:000588582400001 |
出版者 | SPRINGER |
七大方向——子方向分类 | 人工智能+制造 |
国重实验室规划方向分类 | 小样本高噪声数据学习 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/41765 |
专题 | 中科院工业视觉智能装备工程实验室_工业智能技术与系统 |
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,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. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
s10845-020-01709-4.p(1084KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论