A condition-based maintenance policy and input parameters estimation for deteriorating systems under periodic inspection

• Published in: Computers & industrial engineering 2011-10, Vol.61 (3), p.503-511
• Main Authors: Neves, Maxstaley L., Santiago, Leonardo P., Maia, Carlos A.
• Format: Article
• Language: English
• Subjects: Condition-based maintenance; Decision-making under uncertainty; Deterioration; Empirical analysis; Hidden Markov Models; Inspection; Inspections; Maintenance; Mathematical models; Optimal control; Optimization; Optimization techniques; Parameter estimation; Policies; Preventive maintenance; Stochastic-dynamic programming; Studies
• ISSN: 0360-8352; 1879-0550
• DOI: 10.1016/j.cie.2011.04.005

► This paper expands the use of Hidden Markov Model theory in reliability and maintenance field. ► We combine an optimization model using Markov chain with input parameters estimation from empirical data. ► We explore the problem of how to estimate the model input parameters and then adequate them to the empirical data available. ► The main result of this paper is a framework that combines optimization and model parameter computation from historical data. 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.