考虑不完善预防性维修的多目标优化维修策略——以铁路VOBC为例

IF 1.7 4区 工程技术 Q3 ENGINEERING, INDUSTRIAL
Cong Peng, W. Shangguan, B. Cai, Bin Chen
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引用次数: 0

摘要

系统的预防性维护程序旨在提高长期运行性能并降低维护成本。本文试图在系统可靠性和维护成本之间寻求平衡,探索适用于不同现场情况的最优多目标维护策略。我们首先概述了问题,并分析了各种不同的维护活动对可靠性的影响。然后,定义并推导了可靠性损失和维修费用的数学模型,并将其用于构建多目标维修策略。在本研究中,提出PSO-SA随机优化算法来发现Pareto边界,以找到最优的预防性维修阈值和检查间隔,使可靠性损失和维修成本最小。最后,以铁路VOBC为例进行了实例研究。分析结果表明,所提出的多目标维修策略能够满足不同决策者的偏好。它适用于不同的维护需求,可以支持现场维护策略的发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-objective optimal maintenance strategy considering imperfect preventive maintenance: A case study on railway VOBC
Preventive maintenance procedures for systems are designed to improve long-term operating performance and reduce maintenance costs. This paper attempts to seek a trade-off between system reliability and maintenance costs and explores the optimal multi-objective maintenance strategy applicable to different field situations. We first provide an overview of the problem and analyze the impacts of varying different maintenance activities on reliability. Then, a mathematical model of reliability losses and maintenance costs is defined and derived, which will be used to construct a multi-objective maintenance strategy. In this research, the PSO-SA stochastic optimization algorithm is proposed to discover the Pareto frontier to find the optimal preventive maintenance threshold and inspection interval that minimizes reliability loss and maintenance costs. Finally, a case study is performed with railway VOBC as an illustration. The analysis results illustrate that the proposed multi-objective maintenance strategy can satisfy the preferences of various decision-makers. It is suitable for different maintenance needs and can support the development of on-site maintenance strategies.
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来源期刊
CiteScore
4.50
自引率
19.00%
发文量
81
审稿时长
6-12 weeks
期刊介绍: The Journal of Risk and Reliability is for researchers and practitioners who are involved in the field of risk analysis and reliability engineering. The remit of the Journal covers concepts, theories, principles, approaches, methods and models for the proper understanding, assessment, characterisation and management of the risk and reliability of engineering systems. The journal welcomes papers which are based on mathematical and probabilistic analysis, simulation and/or optimisation, as well as works highlighting conceptual and managerial issues. Papers that provide perspectives on current practices and methods, and how to improve these, are also welcome
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