Please use this identifier to cite or link to this item: https://doi.org/10.1080/0740817X.2012.706376
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dc.titleCondition monitoring and remaining useful life prediction using degradation signals: Revisited
dc.contributor.authorChen, N.
dc.contributor.authorTsui, K.L.
dc.date.accessioned2014-06-17T06:59:44Z
dc.date.available2014-06-17T06:59:44Z
dc.date.issued2013-09-01
dc.identifier.citationChen, N., Tsui, K.L. (2013-09-01). Condition monitoring and remaining useful life prediction using degradation signals: Revisited. IIE Transactions (Institute of Industrial Engineers) 45 (9) : 939-952. ScholarBank@NUS Repository. https://doi.org/10.1080/0740817X.2012.706376
dc.identifier.issn0740817X
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/63063
dc.description.abstractCondition monitoring is an important prognostic tool to determine the current operation status of a system/device and to estimate the distribution of the remaining useful life. This article proposes a two-phase model to characterize the degradation process of rotational bearings. A Bayesian framework is used to integrate historical data with up-to-date in situ observations of new working units to improve the degradation modeling and prediction. A new approach is developed to compute the distribution of the remaining useful life based on the degradation signals, which is more accurate compared with methods reported in the literature. Finally, extensive numerical results demonstrate that the proposed framework is effective and efficient. © 2013 Taylor & Francis Group, LLC.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1080/0740817X.2012.706376
dc.sourceScopus
dc.subjectBayesian
dc.subjectCondition monitoring
dc.subjectdegradation
dc.subjectremaining useful life
dc.typeArticle
dc.contributor.departmentINDUSTRIAL & SYSTEMS ENGINEERING
dc.description.doi10.1080/0740817X.2012.706376
dc.description.sourcetitleIIE Transactions (Institute of Industrial Engineers)
dc.description.volume45
dc.description.issue9
dc.description.page939-952
dc.description.codenIIETD
dc.identifier.isiut000319379600001
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