Modelling condition-based maintenance for nonidentical two-component systems considering four types of dependencies

Abstract

Purpose

The move toward Industry 4 is accelerated by the availability of affordable sensing technologies and networking infrastructure. Condition-Based Maintenance is the well-suited maintenance strategy to make use of the information available on assets condition to optimize maintenance interventions. However, devising the optimum maintenance policy requires a representative model of the maintenance system. Most of the existing research has been focusing on single-component systems. However, assets nowadays are complex and composed of many components. The modeling of multicomponent maintenance systems presents various challenges, especially if interactions between components, such as stochastic, structural, economic and resource dependencies are considered.

Design/methodology/approach

In this paper, we present a detailed modeling approach based on Discrete Event Simulation for nonidentical two-component systems subject to Condition-Based Maintenance considering all four types of dependencies.

Findings

The research has shown that optimizing the maintenance system without considering resource dependence led to different and better solutions. In addition, there is a trade-off between maintenance cost and asset availability, confirming the need for multiobjective optimization.

Originality/value

This paper outlines a modeling approach of CBM for nonidentical two-component systems considering stochastic, structural, economic and resource dependencies. A demonstration on a case study is followed where multiobjective optimization was applied to obtain the optimal maintenance policy while minimizing maintenance cost and maximizing asset availability simultaneously.

Citation

Alrabghi, A. (2021), "Modelling condition-based maintenance for nonidentical two-component systems considering four types of dependencies", Journal of Quality in Maintenance Engineering, Vol. 27 No. 1, pp. 169-186. https://doi.org/10.1108/JQME-09-2019-0091