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.
@article{arxiv.1902.03495,
title = {Evaluating reliability of complex systems for Predictive maintenance},
author = {Dongjin Lee and Rong Pan},
journal= {arXiv preprint arXiv:1902.03495},
year = {2020}
}
Comments
7 pages, This is a Conference paper submitted to Industrial and Systems Engineering Research Conference 2016 (ISERC)