A condition-based opportunistic maintenance policy integrated with energy efficiency for two-component parallel systems

Aiping Jiang, Yuanyuan Wang, Yide Cheng

Abstract


Purpose: In order to improve the energy utilization and achieve sustainable development, this paper integrates energy efficiency into condition-based maintenance(CBM) decision-making for two-component parallel systems. The objective is to obtain the optimal maintenance policy by minimizing total cost.

Design/methodology/approach: Based on energy efficiency, the paper considers the economic dependence between the two components to take opportunistic maintenance. Specifically, the objective function consists of traditional maintenance cost and energy cost incurred by energy consumption of components. In order to assess the performance of the proposed new maintenance policy, the paper uses Monte-Carlo method to evaluate the total cost and find the optimal maintenance policy.

Findings: Simulation results indicate that the new maintenance policy is superior to the classical condition-based opportunistic maintenance policy in terms of total economic costs.

Originality/value: For two-component parallel systems, previous researches usually simply establish a condition-based opportunistic maintenance model based on real deterioration data, but ignore energy consumption, energy efficiency (EE) and their contributions of sustainable development. This paper creatively takes energy efficiency into condition-based maintenance(CBM) decision-making process, and proposes a new condition-based opportunistic maintenance policy by using energy efficiency indicator(EEI).


Keywords


energy efficiency, condition-based opportunistic maintenance, two-component parallel systems

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DOI: http://dx.doi.org/10.3926/jiem.2649


Licencia de Creative Commons 

This work is licensed under a Creative Commons Attribution 4.0 International License

Journal of Industrial Engineering and Management, 2008-2018

Online ISSN: 2013-0953; Print ISSN: 2013-8423; Online DL: B-28744-2008

Publisher: OmniaScience