Evaluating reliability of complex systems for Predictive maintenance

Dongjin Lee, Rong Pan

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations


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)
Number of pages7
StatePublished - 2020
Event2016 Industrial and Systems Engineering Research Conference, ISERC 2016 - Anaheim, United States
Duration: May 21 2016May 24 2016


Conference2016 Industrial and Systems Engineering Research Conference, ISERC 2016
Country/TerritoryUnited States


  • Bayesian network
  • Discrete time Markov chain
  • System reliability

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering


Dive into the research topics of 'Evaluating reliability of complex systems for Predictive maintenance'. Together they form a unique fingerprint.

Cite this