A predictive multi-objective condition-based maintenance (CBM) policy considering ecological factors

Abstract

Purpose

The purpose of this paper is to propose an improved multi-objective optimization model for the condition-based maintenance (CBM) of single-component systems which considers periodic imperfect maintenance and ecological factors.

Design/methodology/approach

Based on the application of non-periodic preventive CBM, two recursion models are built for the system: hazard rate and the environmental degradation factor. This paper also established an optimal multi-objective model with a normalization process. The multiple-attribute value theory is used to obtain the optimal preventive maintenance (PM) interval. The simulation and sensitivity analyses are applied to obtain further rules.

Findings

An increase in the number of the occurrences could shorten the duration of a maintenance cycle. The maintenance techniques and maintenance efficiency could be improved by increasing system availability, reducing cost rate and improving degraded condition.

Practical implications

In reality, a variety of environmental situations may occur subsequent to the operations of an advanced manufacturing system. This model could be applied in real cases to help the manufacturers better discover the optimal maintenance cycle with minimized cost and degraded condition of the environment, helping the corporations better fulfill their CSR as well.

Originality/value

Previous research on single-component condition-based predictive maintenance usually focused on the maintenance costs and availability of a system, while ignoring the possible pollution from system operations. This paper proposed a modified multi-objective optimization model considering environment influence which could more comprehensively analyze the factors affecting PM interval.

Citation

Cai, R., Yang, J., Xu, X. and Jiang, A. (2023), "A predictive multi-objective condition-based maintenance (CBM) policy considering ecological factors", Journal of Quality in Maintenance Engineering, Vol. 29 No. 2, pp. 530-552. https://doi.org/10.1108/JQME-02-2022-0010