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Prognostic modelling for industrial asset health management

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dc.contributor.authorGorjian Jolfaei, N.
dc.contributor.authorRameezdeen, R.
dc.contributor.authorGorjian, N.
dc.contributor.authorJin, B.
dc.contributor.authorChow, C.W.K.
dc.date.issued2022
dc.descriptionPublished online: 25 Mar 2022.
dc.description.abstractFailure prognostics and health management are central to the Remaining Useful Life (RUL) estimation of critical engineering assets, particularly to improve safety, reduce downtimes and maintenance expenditures. Over recent years, several prognostic approaches have been developed to predict remaining asset lifetime, optimise maintenance schedules, and enhance equipment availability and reliability. While academic research in this area has grown rapidly, implementations of these methods by industry’s asset managers and reliability experts have only had limited success. Yet asset lifetime and reliability analysis are only restricted to the conventional reliability-centred maintenance and total productive maintenance approaches in industries. The purpose of this paper is to emphasise a need for a paradigm shift in industrial asset health management from the conventional to modern approaches that would benefit industries. At first, this paper classifies existing prognostic techniques into the traditional reliability, model-based, and datadriven approaches. Each prognostic approach is then analytically discussed with emphasis on models and algorithms. Consequently, this paper explores the strengths and weaknesses of main models in these groups to assist industry practitioners to select an appropriate prognostic model for RUL prediction within their specific business environment. Finally, the paper concludes with a brief discussion on possible future trends and further research directions in this field.
dc.description.statementofresponsibilityNeda Gorjian Jolfaei, Raufdeen Rameezdeen, Nima Gorjian, Bo Jin and Christopher W. K. Chow
dc.identifier.citationSafety and Reliability, 2022; 41(1):45-97
dc.identifier.doi10.1080/09617353.2022.2051140
dc.identifier.issn0961-7353
dc.identifier.issn2469-4126
dc.identifier.orcidGorjian Jolfaei, N. [0000-0001-6022-6210]
dc.identifier.urihttps://hdl.handle.net/2440/135361
dc.language.isoen
dc.publisherTaylor & Francis
dc.rights© 2022 Safety and Reliability Society
dc.source.urihttps://doi.org/10.1080/09617353.2022.2051140
dc.subjectAsset health management; Condition-based maintenance; Fault diagnostics; Failure prognostics; Reliability modelling
dc.titlePrognostic modelling for industrial asset health management
dc.typeJournal article
pubs.publication-statusPublished

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