Maintenance strategy optimization using a continuous-state partially observable semi-Markov decision process
Due to the limitation of current condition monitoring technologies, the estimates of asset health states may contain some uncertainties. A maintenance strategy ignoring this uncertainty of asset health state can cause additional costs or downtime. The partially observable Markov decision process (POMDP) is a commonly used approach to derive optimal maintenance strategies when asset health inspections are imperfect. However, existing applications of the POMDP to maintenance decision-making largely adopt the discrete time and state assumptions. The discrete-time assumption requires the health state transitions and maintenance activities only happen at discrete epochs, which cannot model the failure time accurately and is not cost-effective. The discrete health state assumption, on the other hand, may not be elaborate enough to improve the effectiveness of maintenance. To address these limitations, this paper proposes a continuous state partially observable semi-Markov decision process (POSMDP). An algorithm that combines the Monte Carlo-based density projection method and the policy iteration is developed to solve the POSMDP. Different types of maintenance activities (i.e., inspections, replacement, and imperfect maintenance) are considered in this paper. The next maintenance action and the corresponding waiting durations are optimized jointly to minimize the long-run expected cost per unit time and availability. The result of simulation studies shows that the proposed maintenance optimization approach is more cost-effective than maintenance strategies derived by another two approximate methods, when regular inspection intervals are adopted. The simulation study also shows that the maintenance cost can be further reduced by developing maintenance strategies with state-dependent maintenance intervals using the POSMDP. In addition, during the simulation studies the proposed POSMDP shows the ability to adopt a cost-effective strategy structure when multiple types of maintenance activities are involved.
|Year of publication:||
|Authors:||Zhou, Yifan ; Ma, Lin ; Mathew, Joseph ; Sun, Yong ; Wolff, Rodney C.|
|Subject:||ELECTRICAL AND ELECTRONIC ENGINEERING | asset health state | Markov decision process (POMDP) | discrete-time assumption|
|Type of publication:||Article|
Zhou, Yifan, Ma, Lin, Mathew, Joseph, Sun, Yong, & Wolff, Rodney C. (2011) Maintenance strategy optimization using a continuous-state partially observable semi-Markov decision process. Microelectronics Reliability, 51(2), pp. 300-309.
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