Evaluating reliability of complex systems for Predictive maintenance

Dongjin Lee, Rong Pan

Research output: Contribution to conferencePaper

Abstract

Predictive Maintenance (PdM) can only be implemented when the online knowledge of system condition is available, and this has become available with deployment of on-equipment sensors. To date, most studies on predicting the remaining useful lifetime of a system have been focusing on either single-component systems or systems with deterministic reliability structures. This assumption is not applicable on some realistic problems, where there exist uncertainties in reliability structures of complex systems. In this paper, a PdM scheme is developed by employing a Discrete Time Markov Chain (DTMC) for forecasting the health of monitored components and a Bayesian Network (BN) for modeling the multi-component system reliability. Therefore, probabilistic inferences on both the system and its components' status can be made and PdM can be scheduled on both levels.

Original languageEnglish (US)
Pages1034-1040
Number of pages7
StatePublished - 2020
Externally publishedYes
Event2016 Industrial and Systems Engineering Research Conference, ISERC 2016 - Anaheim, United States
Duration: May 21 2016May 24 2016

Conference

Conference2016 Industrial and Systems Engineering Research Conference, ISERC 2016
CountryUnited States
CityAnaheim
Period5/21/165/24/16

Keywords

  • Bayesian network
  • Discrete time Markov chain
  • System reliability

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

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    Lee, D., & Pan, R. (2020). Evaluating reliability of complex systems for Predictive maintenance. 1034-1040. Paper presented at 2016 Industrial and Systems Engineering Research Conference, ISERC 2016, Anaheim, United States.