Domain generalization for cross-domain fault diagnosis: An application-oriented perspective and a benchmark study

•The first taxonomy for domain generalization-based fault diagnosis is proposed.•A basic and reproducible code framework is provided.•A broad discussion of critical challenges and future directions is presented. Most data-driven methods for fault diagnostics rely on the assumption of independently a...

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Veröffentlicht in:	Reliability engineering & system safety Jg. 245; S. 109964
Hauptverfasser:	Zhao, Chao, Zio, Enrico, Shen, Weiming
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.05.2024 Elsevier
Schlagworte:	Deep learning Domain generalization Domain shift Engineering Sciences Fault diagnosis Fault diagnosis Domain shift Deep learning Domain generalization
ISSN:	0951-8320, 1879-0836
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Abstract	•The first taxonomy for domain generalization-based fault diagnosis is proposed.•A basic and reproducible code framework is provided.•A broad discussion of critical challenges and future directions is presented. Most data-driven methods for fault diagnostics rely on the assumption of independently and identically distributed data of training and testing. However, domain shift between the phases of training and testing is common in practice. Recently, domain generalization-based fault diagnosis (DGFD) has gained widespread attention for learning fault diagnosis knowledge from multiple source domains and applying it to unseen target domains. This paper summarizes the developments in DGFD from an application-oriented perspective. Firstly, basic definitions of DGFD and its variant applications are formulated. Then, motivations, goals, challenges and state-of-the-art solutions for different applications are discussed. The limitations of existing technologies are highlighted. A comprehensive benchmark study is carried out on eight open-source and two self-collected datasets to provide an understanding of the existing methods and a unified framework for researchers. Finally, several future directions are given. Our code is available at https://github.com/CHAOZHAO-1/DG-PHM.
AbstractList	•The first taxonomy for domain generalization-based fault diagnosis is proposed.•A basic and reproducible code framework is provided.•A broad discussion of critical challenges and future directions is presented. Most data-driven methods for fault diagnostics rely on the assumption of independently and identically distributed data of training and testing. However, domain shift between the phases of training and testing is common in practice. Recently, domain generalization-based fault diagnosis (DGFD) has gained widespread attention for learning fault diagnosis knowledge from multiple source domains and applying it to unseen target domains. This paper summarizes the developments in DGFD from an application-oriented perspective. Firstly, basic definitions of DGFD and its variant applications are formulated. Then, motivations, goals, challenges and state-of-the-art solutions for different applications are discussed. The limitations of existing technologies are highlighted. A comprehensive benchmark study is carried out on eight open-source and two self-collected datasets to provide an understanding of the existing methods and a unified framework for researchers. Finally, several future directions are given. Our code is available at https://github.com/CHAOZHAO-1/DG-PHM. Most data-driven methods for fault diagnostics rely on the assumption of independently and identically distributed data of training and testing. However, domain shift between the phases of training and testing is common in practice. Recently, domain generalization-based fault diagnosis (DGFD) has gained widespread attention for learning fault diagnosis knowledge from multiple source domains and applying it to unseen target domains. This paper summarizes the developments in DGFD from an application-oriented perspective. Firstly, basic definitions of DGFD and its variant applications are formulated. Then, motivations, goals, challenges and state-of-the-art solutions for different applications are discussed. The limitations of existing technologies are highlighted. A comprehensive benchmark study is carried out on eight open-source and two self-collected datasets to provide an understanding of the existing methods and a unified framework for researchers. Finally, several future directions are given. Our code is available at https://github.com/CHAOZHAO-1/DG-PHM.
ArticleNumber	109964
Author	Shen, Weiming Zhao, Chao Zio, Enrico
Author_xml	– sequence: 1 givenname: Chao surname: Zhao fullname: Zhao, Chao organization: State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science & Technology, Wuhan 430074, China – sequence: 2 givenname: Enrico surname: Zio fullname: Zio, Enrico organization: MINES Paris PSL University, CRC, Sophia Antipolis, France – sequence: 3 givenname: Weiming surname: Shen fullname: Shen, Weiming email: shenwm@hust.edu.cn organization: State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science & Technology, Wuhan 430074, China
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Keywords	Fault diagnosis Domain shift Deep learning Domain generalization
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Snippet	•The first taxonomy for domain generalization-based fault diagnosis is proposed.•A basic and reproducible code framework is provided.•A broad discussion of... Most data-driven methods for fault diagnostics rely on the assumption of independently and identically distributed data of training and testing. However,...
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SubjectTerms	Deep learning Domain generalization Domain shift Engineering Sciences Fault diagnosis
Title	Domain generalization for cross-domain fault diagnosis: An application-oriented perspective and a benchmark study
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