Simulation modeling of a new condition based maintenance model

Kamran Shahanaghi1, Hamid Babaei2, Arash Bakhsha1

1,Department of industrial engineering, Iran University of Science and Technology (IUST), Tehran, Iran
2 Department of industrial engineering, Iran University of Science and Technology (IUST), Iran, Tehran, Narmak, Hengam ave. e-mail:,

In this work we aim to optimize maintenance threshold policy for a stochastically and continuously deteriorating system. We assume that after each preventive maintenance action, not only is the new system state random, but also it is worse than the preceding improved system state. Since this modeling approach avoids underestimating system condition, it results in a maintenance policy which avoids random shocks during real operation of the system and consequently incurs lower failure costs on the system. This assumption is frequently experienced in deteriorating systems like those pumps which are used in petrochemical plants. They often pump at a lower pressure (after a preventive maintenance action) in comparison with the pressure they were pumping after the last previous preventive maintenance action. We treat preventive maintenance and preventive replacement thresholds as decision variables for the supposed system. Due to the computational complexity in estimation of stationary probability distribution of the system state at infinity we use simulation as a powerful tool to optimize our model.

KEYWORDS: Condition Based Maintenance (CBM), Cost Optimization, Failure, Monte Carlo Simulation.

To read the full article,
please log in:
If you haven't registered already,
you can do so for free:
Copyright 2000-2008 Professor F.R. Hall & Dr I. Oraifige, University of Wolverhampton.
close this window