A comprehensive survey on applications of AI technologies to failure analysis of industrial systems

Bi, S; Wang, C; Wu, B; Hu, S; Huang, W; Ni, W; Gong, Y; Wang, X

A comprehensive survey on applications of AI technologies to failure analysis of industrial systems

Bi, S Wang, C Wu, B Hu, S Huang, W Ni, W

Gong, Y Wang, X

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Engineering Failure Analysis, 2023, 148
Issue Date:: 2023-06-01

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Field	Value	Language
dc.contributor.author	Bi, S
dc.contributor.author	Wang, C
dc.contributor.author	Wu, B
dc.contributor.author	Hu, S
dc.contributor.author	Huang, W
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X
dc.contributor.author	Gong, Y
dc.contributor.author	Wang, X
dc.date.accessioned	2024-03-15T20:28:54Z
dc.date.available	2024-03-15T20:28:54Z
dc.date.issued	2023-06-01
dc.identifier.citation	Engineering Failure Analysis, 2023, 148
dc.identifier.issn	1350-6307
dc.identifier.issn	1873-1961
dc.identifier.uri	http://hdl.handle.net/10453/176796
dc.description.abstract	Component reliability plays a pivotal role in industrial systems, which are evolving with larger complexity and higher dimensionality of data. It is insufficient to ensure reliability and prevent failure based only on empiri- cal and parametric assumptions. Driven by huge amount of historical data, data- and statistics-based approaches aided by artificial intelligence (AI) are emerging as promising solutions. Especially, with the introduction to deep learning technology, the powerful ability of hierarchy representation is re- markably enhanced with deep cascaded layers. Furthermore, the demand for AI technology is high, and the applicability of the model in securing reliability, failure prediction and prevention in the industrial system is still nontrivial. Yet, there hardly exists such a systematic review of the AI-based approaches. In this survey, we provide a comprehensive overview of the AI- aided approaches to failure analysis in industrial systems, with sufficient or insufficient data, and imbalanced issues. We provide a concise introduction to the popular AI algorithms, classify the application scenarios of industrial systems into homogeneous or heterogeneous data-based scenarios, and review them respectively. We also summarize the resolved issues, challenges and promising directions.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Engineering Failure Analysis
dc.relation.isbasedon	10.1016/j.engfailanal.2023.107172
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0905 Civil Engineering, 0912 Materials Engineering, 0913 Mechanical Engineering
dc.subject.classification	Mechanical Engineering & Transports
dc.subject.classification	4005 Civil engineering
dc.subject.classification	4016 Materials engineering
dc.subject.classification	4017 Mechanical engineering
dc.title	A comprehensive survey on applications of AI technologies to failure analysis of industrial systems
dc.type	Journal Article
utslib.citation.volume	148
utslib.for	0905 Civil Engineering
utslib.for	0912 Materials Engineering
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2024-03-15T20:28:52Z
pubs.publication-status	Published
pubs.volume	148

Abstract:

Component reliability plays a pivotal role in industrial systems, which are evolving with larger complexity and higher dimensionality of data. It is insufficient to ensure reliability and prevent failure based only on empiri- cal and parametric assumptions. Driven by huge amount of historical data, data- and statistics-based approaches aided by artificial intelligence (AI) are emerging as promising solutions. Especially, with the introduction to deep learning technology, the powerful ability of hierarchy representation is re- markably enhanced with deep cascaded layers. Furthermore, the demand for AI technology is high, and the applicability of the model in securing reliability, failure prediction and prevention in the industrial system is still nontrivial. Yet, there hardly exists such a systematic review of the AI-based approaches. In this survey, we provide a comprehensive overview of the AI- aided approaches to failure analysis in industrial systems, with sufficient or insufficient data, and imbalanced issues. We provide a concise introduction to the popular AI algorithms, classify the application scenarios of industrial systems into homogeneous or heterogeneous data-based scenarios, and review them respectively. We also summarize the resolved issues, challenges and promising directions.

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http://hdl.handle.net/10453/176796