Recent advances in mechanism/data-driven fault diagnosis of complex engineering systems with uncertainties
The relentless advancement of modern technology has given rise to increasingly intricate and sophisticated engineering systems, which in turn demand more reliable and intelligent fault diagnosis methods. This paper presents a comprehensive review of fault diagnosis in uncertain environments, focusin...
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| Published in | AIMS mathematics Vol. 9; no. 11; pp. 29736 - 29772 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
AIMS Press
01.01.2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2473-6988 2473-6988 |
| DOI | 10.3934/math.20241441 |
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| Abstract | The relentless advancement of modern technology has given rise to increasingly intricate and sophisticated engineering systems, which in turn demand more reliable and intelligent fault diagnosis methods. This paper presents a comprehensive review of fault diagnosis in uncertain environments, focusing on innovative strategies for intelligent fault diagnosis. To this end, conventional fault diagnosis methods are first reviewed, including advances in mechanism-driven, data-driven, and hybrid-driven diagnostic models and their strengths, limitations, and applicability across various scenarios. Subsequently, we provide a thorough exploration of multi-source uncertainty in fault diagnosis, addressing its generation, quantification, and implications for diagnostic processes. Then, intelligent strategies for all stages of fault diagnosis starting from signal acquisition are highlighted, especially in the context of complex engineering systems. Finally, we conclude with insights and perspectives on future directions in the field, emphasizing the need for the continued evolution of intelligent diagnostic systems to meet the challenges posed by modern engineering complexities. |
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| AbstractList | The relentless advancement of modern technology has given rise to increasingly intricate and sophisticated engineering systems, which in turn demand more reliable and intelligent fault diagnosis methods. This paper presents a comprehensive review of fault diagnosis in uncertain environments, focusing on innovative strategies for intelligent fault diagnosis. To this end, conventional fault diagnosis methods are first reviewed, including advances in mechanism-driven, data-driven, and hybrid-driven diagnostic models and their strengths, limitations, and applicability across various scenarios. Subsequently, we provide a thorough exploration of multi-source uncertainty in fault diagnosis, addressing its generation, quantification, and implications for diagnostic processes. Then, intelligent strategies for all stages of fault diagnosis starting from signal acquisition are highlighted, especially in the context of complex engineering systems. Finally, we conclude with insights and perspectives on future directions in the field, emphasizing the need for the continued evolution of intelligent diagnostic systems to meet the challenges posed by modern engineering complexities. |
| Author | Chen, Xinxing Qiang, Xin Li, Shaohua Wang, Chong Fan, Haoran |
| Author_xml | – sequence: 1 givenname: Chong surname: Wang fullname: Wang, Chong organization: Tianmushan Laboratory, Yuhang District, Hangzhou 311115, China, National Key Laboratory of Strength and Structural Integrity, Institute of Solid Mechanics, School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China – sequence: 2 givenname: Xinxing surname: Chen fullname: Chen, Xinxing organization: Tianmushan Laboratory, Yuhang District, Hangzhou 311115, China, National Key Laboratory of Strength and Structural Integrity, Institute of Solid Mechanics, School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China – sequence: 3 givenname: Xin surname: Qiang fullname: Qiang, Xin organization: Tianmushan Laboratory, Yuhang District, Hangzhou 311115, China, National Key Laboratory of Strength and Structural Integrity, Institute of Solid Mechanics, School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China – sequence: 4 givenname: Haoran surname: Fan fullname: Fan, Haoran organization: Tianmushan Laboratory, Yuhang District, Hangzhou 311115, China, National Key Laboratory of Strength and Structural Integrity, Institute of Solid Mechanics, School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China – sequence: 5 givenname: Shaohua surname: Li fullname: Li, Shaohua organization: Beijing Institute of Spacecraft Environment Engineering, Beijing 100094, China |
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| Title | Recent advances in mechanism/data-driven fault diagnosis of complex engineering systems with uncertainties |
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