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 language | English (US) |
|---|---|
| Pages | 1034-1040 |
| Number of pages | 7 |
| State | Published - 2020 |
| Event | 2016 Industrial and Systems Engineering Research Conference, ISERC 2016 - Anaheim, United States Duration: May 21 2016 → May 24 2016 |
Conference
| Conference | 2016 Industrial and Systems Engineering Research Conference, ISERC 2016 |
|---|---|
| Country/Territory | United States |
| City | Anaheim |
| Period | 5/21/16 → 5/24/16 |
Keywords
- Bayesian network
- Discrete time Markov chain
- System reliability
ASJC Scopus subject areas
- Control and Systems Engineering
- Industrial and Manufacturing Engineering