This paper combines an optimization model and input parameters estimation from empirical data, in order to propose condition-based maintenance policies. The system deterioration is described by discrete states ordered from the state “as good as new” to the state “completely failed”. At each periodic inspection, whose outcome might not be accurate, a decision has to be made between continuing to operate the system or stopping and performing its preventive maintenance. We explore the problem of how to estimate the model input parameters, i.e., how to adequate the model inputs to the empirical data available. For this purpose, we use the Hidden Markov Model theory. The literature has not explored the combination of optimization techniques and model input parameters, through historical data, for problems with imperfect information such as the one considered in this paper. We thoroughly discuss our approach, illustrate it with empirical data and also point out directions for future research.
|Tidsskrift||Computers & Industrial Engineering|
|Status||Udgivet - 2011|
- Condition-based maintenance
- Stochastic-dynamic programming
- Optimal control
- Hidden Markov Models
- Decision-making under uncertainty