On-line reliability assessment for an electronic system subject to condition monitoring

Shuai Zhao, Viliam Makis, Shaowei Chen, Yong Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

Abstract

We present a new approach for the on-line reliability assessment of an electronic system subject to condition monitoring. In this paper, the degradation of the electronic system is driven by a nonlinear Wiener process with a time drift, which is incorporated into the proportional hazards model to describe the hazard rate of the time to failure. Using the discretization of the degradation path and the time axis, closed-form approximations for the reliability quantities are obtained using the transition probability matrix. Unlike the conventional method which is applicable only for a small number of degradation states, the calculation of these quantities can be accomplished with just the basic manipulation of the transition matrix, which is computationally efficient and applicable to real-time conditional reliability calculation for a general number of states. The effectiveness of the proposed approach is demonstrated by a numerical study.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Prognostics and Health Management, ICPHM 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509003822
DOIs
StatePublished - 12 Aug 2016
Event2016 IEEE International Conference on Prognostics and Health Management, ICPHM 2016 - Ottawa, Canada
Duration: 20 Jun 201622 Jun 2016

Publication series

Name2016 IEEE International Conference on Prognostics and Health Management, ICPHM 2016

Conference

Conference2016 IEEE International Conference on Prognostics and Health Management, ICPHM 2016
Country/TerritoryCanada
CityOttawa
Period20/06/1622/06/16

Keywords

  • Nonlinear Wiener process with a time drift
  • online reliability assessment
  • prognostics and health management
  • proportional hazards model
  • remaining useful life prediction

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