Knowledge Commons of Institute of Automation,CAS
Generalization on Unseen Domains via Model-Agnostic Learning for Intelligent Fault Diagnosis | |
Wang, Huanjie1,2; Bai, Xiwei2; Wang, Sihan3; Tan, Jie1,2; Liu, Chengbao2 | |
发表期刊 | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT |
ISSN | 0018-9456 |
2022 | |
卷号 | 71页码:11 |
摘要 | Machine learning-based diagnosis methods have achieved remarkable success under the assumption that the training and test data are identically distributed. However, a critical requirement of these methods is the generalization capability to unseen domains when deployed to actual diagnosis scenarios. We introduce the challenging problem of domain generalization, i.e., learning from multiple source domains to produce a model that can directly generalize to unseen domains without target information. We adopt a model-agnostic learning produce that maximizes the dot product of gradients between the source domains. Such a gradient alignment objective encourages finding a common optimization path for all source domains, which helps to focus on invariant representations. Furthermore, we propose two feature regularizations that explicitly regularize the feature space. Global feature regularization aligns class relationships between different domains to preserve the domain-invariant knowledge. Local feature regularization encourages the model to learn domain-agnostic class-specific representations with intraclass compactness and interclass separability. The effectiveness of the proposed method is demonstrated with generalization experiments on two benchmarks. |
关键词 | Data-driven fault diagnosis Domain generalization Model-agnostic learning Rolling bearing |
DOI | 10.1109/TIM.2022.3152316 |
关键词[WOS] | NETWORK ; KERNEL |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Development Program of China under Grant 2018YFB1703401 ; National Nature Science Foundation of China under Grant 62003344 and Grant U1801263 |
项目资助者 | National Key Research and Development Program of China ; National Nature Science Foundation of China |
WOS研究方向 | Engineering ; Instruments & Instrumentation |
WOS类目 | Engineering, Electrical & Electronic ; Instruments & Instrumentation |
WOS记录号 | WOS:000766618900020 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
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/173211/48148 |
专题 | 中国科学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.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 3.Northeastern Univ, Sch Informat Sci & Engn, Shenyang 110819, 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,Wang, Sihan,et al. Generalization on Unseen Domains via Model-Agnostic Learning for Intelligent Fault Diagnosis[J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,2022,71:11. |
APA | Wang, Huanjie,Bai, Xiwei,Wang, Sihan,Tan, Jie,&Liu, Chengbao.(2022).Generalization on Unseen Domains via Model-Agnostic Learning for Intelligent Fault Diagnosis.IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,71,11. |
MLA | Wang, Huanjie,et al."Generalization on Unseen Domains via Model-Agnostic Learning for Intelligent Fault Diagnosis".IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 71(2022):11. |
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