The SRVM: A Similarity-Based Relevance Vector Machine for Remaining Useful Lifetime Prediction in the Industrial Internet of Things

Li, G; Wu, Y; Wang, C; Peng, S; Niu, J; Yu, S

The SRVM: A Similarity-Based Relevance Vector Machine for Remaining Useful Lifetime Prediction in the Industrial Internet of Things

Li, G Wu, Y Wang, C Peng, S Niu, J Yu, S

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Publisher:: IEEE COMPUTER SOC
Publication Type:: Journal Article
Citation:: IEEE Intelligent Systems, 2023, 38, (5), pp. 45-55
Issue Date:: 2023-09-01

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Field	Value	Language
dc.contributor.author	Li, G
dc.contributor.author	Wu, Y
dc.contributor.author	Wang, C
dc.contributor.author	Peng, S
dc.contributor.author	Niu, J
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.date.accessioned	2024-03-27T05:05:22Z
dc.date.available	2024-03-27T05:05:22Z
dc.date.issued	2023-09-01
dc.identifier.citation	IEEE Intelligent Systems, 2023, 38, (5), pp. 45-55
dc.identifier.issn	1541-1672
dc.identifier.issn	1941-1294
dc.identifier.uri	http://hdl.handle.net/10453/177266
dc.description.abstract	With the continuous advancement of Industry 4.0 and intelligent manufacturing, remaining useful lifetime (RUL) prediction can forecast the future degradation state of machinery and then estimate the remaining service time before it loses its safe operation ability. Accordingly, a series of predictive maintenance strategies can be regulated in advance for equipment in the Industrial Internet of Things. To tackle the challenges of insufficiency of failure data and lack of confidence in RUL prediction results, a similarity-based relevance vector machine (SRVM) is proposed in this article. Primarily, the relationship among latent variables in the SRVM is learned adaptively through similarity computations to fully utilize the limited degradation data. Furthermore, these internal variables in the SRVM are treated as time-varying variables and re-estimated dynamically to provide RUL prediction with reliable confidence. The experiment results show that the prediction accuracy of the SRVM is higher than that of other baseline methods.
dc.language	English
dc.publisher	IEEE COMPUTER SOC
dc.relation.ispartof	IEEE Intelligent Systems
dc.relation.isbasedon	10.1109/MIS.2023.3289067
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0806 Information Systems, 0906 Electrical and Electronic Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	The SRVM: A Similarity-Based Relevance Vector Machine for Remaining Useful Lifetime Prediction in the Industrial Internet of Things
dc.type	Journal Article
utslib.citation.volume	38
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0806 Information Systems
utslib.for	0906 Electrical and Electronic 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 Computer Science
pubs.organisational-group	University of Technology Sydney/Strength - CCSP - Centre for Cyber Security and Privacy
utslib.copyright.status	closed_access	*
dc.date.updated	2024-03-27T05:05:21Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	38
utslib.citation.issue	5

Abstract:

With the continuous advancement of Industry 4.0 and intelligent manufacturing, remaining useful lifetime (RUL) prediction can forecast the future degradation state of machinery and then estimate the remaining service time before it loses its safe operation ability. Accordingly, a series of predictive maintenance strategies can be regulated in advance for equipment in the Industrial Internet of Things. To tackle the challenges of insufficiency of failure data and lack of confidence in RUL prediction results, a similarity-based relevance vector machine (SRVM) is proposed in this article. Primarily, the relationship among latent variables in the SRVM is learned adaptively through similarity computations to fully utilize the limited degradation data. Furthermore, these internal variables in the SRVM are treated as time-varying variables and re-estimated dynamically to provide RUL prediction with reliable confidence. The experiment results show that the prediction accuracy of the SRVM is higher than that of other baseline methods.

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http://hdl.handle.net/10453/177266