An intelligent condition‐based maintenance scheduling model
International Journal of Quality & Reliability Management
ISSN: 0265-671X
Article publication date: 20 March 2007
Abstract
Purpose
Condition‐based maintenance (CBM) has increasingly drawn attention in industry because of its many benefits. The CBM problem is a kind of state‐dependent scheduling problem, and is very hard to solve within the conventional Markov decision process framework. The purpose of this paper is to present an intelligent CBM scheduling model for which incremental decision tree learning as an evolutionary system identification model and dynamic programming as a control model are developed.
Design/methodology/approach
To fully exploit the merits of CBM, this paper models CBM scheduling as a state‐dependent, sequential decision‐making problem. The objective function is formulated as the minimization of the total maintenance cost. Instead of interpreting the problem within the widely used Markovian framework, this paper proposes an intelligent maintenance scheduling approach that integrates an incremental decision tree learning method and deterministic dynamic programming techniques.
Findings
Although the intelligent maintenance scheduling approach proposed in this paper does not guarantee an optimal scheduling policy from a mathematical viewpoint, it is verified through a simulation‐based experiment that the intelligent maintenance scheduler is capable of providing a good scheduling policy that can be used in practice.
Originality/value
This paper presents an intelligent maintenance scheduler. As a system identification model, we devise a new incremental decision tree learning method by which interaction patterns among attributes and machine condition are disclosed in an evolutionary manner. A deterministic dynamic programming technique is then applied to select the best safe state in terms of the total maintenance cost.
Keywords
Citation
Baek, J. (2007), "An intelligent condition‐based maintenance scheduling model", International Journal of Quality & Reliability Management, Vol. 24 No. 3, pp. 312-327. https://doi.org/10.1108/02656710710730898
Publisher
:Emerald Group Publishing Limited
Copyright © 2007, Emerald Group Publishing Limited