Optimal condition based maintenance with imperfect information and the proportional hazards model

Condition based maintenance (CBM) is based on collecting observations over time, in order to assess equipment's state, to prevent its failure and to determine the optimal maintenance strategies. In this paper, we derive an optimal CBM replacement policy when the state of equipment is unknown bu...

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Bibliographic Details
Published in:International journal of production research 2007-02, Vol.45 (4), p.989-1012
Main Authors: Ghasemi, A., Yacout, S., Ouali, M. S.
Format: Article
Language:English
Subjects:
Applied sciences
Condition monitoring
Decision theory. Utility theory
Exact sciences and technology
Maintenance
Markov analysis
Markov modelling
Operational research and scientific management
Operational research. Management science
Optimization
Preventive maintenance
Reliability theory. Replacement problems
Replacement policies
Stochastic models
Studies
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Summary:Condition based maintenance (CBM) is based on collecting observations over time, in order to assess equipment's state, to prevent its failure and to determine the optimal maintenance strategies. In this paper, we derive an optimal CBM replacement policy when the state of equipment is unknown but can be estimated based on observed condition. We use a proportional hazards model (PHM) to represent the system's degradation. Since equipment's state is unknown, the optimization of the optimal maintenance policy is formulated as a partially observed Markov decision process (POMDP), and the problem is solved using dynamic programming. Practical advantages of combining the PHM with the POMDP are shown.
ISSN:0020-7543
1366-588X
DOI:10.1080/00207540600596882