Condition-based maintenance optimization for deteriorating systems considering performance-based contracting and destructive inspections

IF 1.7 4区 工程技术 Q3 ENGINEERING, INDUSTRIAL
Yu-Jun Zhang, Yukun Wang, Xiaopeng Li, Yiliu Liu, Weizheng Gao
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引用次数: 0

Abstract

As a novel contracting approach, the performance-based contracting (PBC) utilizes defined performance goals and structured incentives to improve the system availability and reduce the cost by tying the compensation to the service supplier to the system performance outcome. In this paper, two new CBM optimization models within the PBC framework are proposed considering the impacts of destructive inspections on the system degradation behavior. The average maintenance cost rate and the system availability within the contract horizon are estimated. Under the inspection-based replacement scheme, the objectives are to maximize the expected profit rate to the supplier and/or the resulting system average availability. A solution procedure based on the non-dominated sorting genetic algorithm (NSGA-II) combining with the weighted sum method (WSM) is introduced to derive the optimal CBM policies. Numerical examples and sensitivity analysis are conducted to examine the applicability of the proposed models.
考虑到基于性能的合同和破坏性检查,对恶化系统进行基于状态的维护优化
作为一种新颖的合同方式,基于绩效的合同(PBC)利用明确的绩效目标和结构化的激励机制,通过将服务供应商的报酬与系统绩效结果联系起来,来提高系统可用性并降低成本。在PBC框架下,考虑破坏检测对系统退化行为的影响,提出了两种新的CBM优化模型。估算了合同期内的平均维护成本率和系统可用性。在基于检查的更换方案下,目标是最大化供应商的预期利润率和/或由此产生的系统平均可用性。提出了一种基于非支配排序遗传算法(NSGA-II)与加权和法(WSM)相结合的求解过程,推导出最优CBM策略。通过数值算例和灵敏度分析验证了所提模型的适用性。
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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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