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

► 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 t...

Full description

Saved in:
Bibliographic Details
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
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:► 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.
ISSN:0360-8352
1879-0550
DOI:10.1016/j.cie.2011.04.005