Condition monitoring and remaining useful life prediction using degradation signals : Revisited

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

139 Scopus Citations
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Author(s)

  • Nan Chen
  • Kwok Leung Tsui

Detail(s)

Original languageEnglish
Pages (from-to)939-952
Journal / PublicationIIE Transactions (Institute of Industrial Engineers)
Volume45
Issue number9
Publication statusPublished - 1 Sep 2013

Abstract

Condition 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.

Research Area(s)

  • Bayesian, Condition monitoring, degradation, remaining useful life