2019/02/09 by Dongjin Lee, Lee, Dongjin, Rong Pan +1
Computer Science · Decision Sciences · Engineering · #FOS: Electrical engineering #Reliability and Maintenance Optimization #Risk and Safety Analysis #Software Reliability and Analysis Research #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1902.03495
openalex publication_date 2019/02/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.