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Optimization and Selection of Maintenance Policies in an Electrical Gas Turbine Generator Based on the Hybrid Reliability-Centered Maintenance (RCM) Model
Processes ( IF 2.8 ) Pub Date : 2020-06-04 , DOI: 10.3390/pr8060670
Moath Alrifaey , Tang Sai Hong , Azizan As’arry , Eris Elianddy Supeni , Chun Kit Ang

The electrical generation industry is looking for techniques to precisely determine the proper maintenance policy and schedule of their assets. Reliability-centered maintenance (RCM) is a methodology for choosing what maintenance activities have to be performed to keep the asset working within its designed function. Current developments in RCM models are struggling to solve the drawbacks of traditional RCM with regards to optimization and strategy selection; for instance, traditional RCM handles each failure mode individually with a simple yes or no safety question in which question has the possibility of major error and missing the effect of a combinational failure mode. Hence, in the present study, a hybrid RCM model was proposed to fill these gaps and find the optimal maintenance policies and scheduling by a combination of hybrid linguistic-failure mode and effect analysis (HL-FMEA), the co-evolutionary multi-objective particle swarm optimization (CMPSO) algorithm, an analytic network process (ANP), and developed maintenance decision tree (DMDT). To demonstrate the effectiveness and efficiencies of the proposed RCM model, a case study on the maintenance of an electrical generator was conducted at a Yemeni oil and gas processing plant. The results confirm that, compared with previous studies, the proposed model gave the optimal maintenance policies and scheduling for the electrical generator in a well-structured plan, economically and effectively.

中文翻译:

基于混合可靠性中心维护(RCM)模型的燃气轮机维护策略的优化与选择

发电行业正在寻找能够精确确定正确维护策略和资产时间表的技术。以可靠性为中心的维护(RCM)是一种方法,用于选择必须执行哪些维护活动才能使资产在其设计功能范围内工作。RCM模型的最新发展正努力解决传统RCM在优化和策略选择方面的弊端。例如,传统的RCM通过一个简单的“是”或“否”安全问题来单独处理每种故障模式,其中该问题有可能出现重大错误并错过组合故障模式的影响。因此,在本研究中,提出了一种混合RCM模型,通过混合语言-故障模式和效果分析(HL-FMEA),共同进化多目标粒子群优化(CMPSO)算法来填补这些空白并找到最佳的维护策略和调度,分析网络流程(ANP)和开发的维护决策树(DMDT)。为了证明所提出的RCM模型的有效性和效率,在也门石油和天然气加工厂进行了发电机维护的案例研究。结果证实,与先前的研究相比,该模型在结构合理的计划中经济有效地给出了发电机的最佳维护策略和调度。协同进化多目标粒子群算法(CMPSO),分析网络过程(ANP)和已开发的维护决策树(DMDT)。为了证明所提出的RCM模型的有效性和效率,在也门石油和天然气加工厂进行了发电机维护的案例研究。结果证实,与先前的研究相比,该模型在结构合理的计划中经济有效地给出了发电机的最佳维护策略和调度。协同进化多目标粒子群算法(CMPSO),分析网络过程(ANP)和已开发的维护决策树(DMDT)。为了证明所提出的RCM模型的有效性和效率,在也门石油和天然气加工厂进行了发电机维护的案例研究。结果证实,与先前的研究相比,该模型在结构合理的计划中经济有效地给出了发电机的最佳维护策略和调度。在也门石油和天然气加工厂进行了发电机维护的案例研究。结果证实,与先前的研究相比,该模型在结构合理的计划中经济有效地给出了发电机的最佳维护策略和调度。在也门石油和天然气加工厂进行了发电机维护的案例研究。结果证实,与先前的研究相比,该模型在结构合理的计划中经济有效地给出了发电机的最佳维护策略和调度。
更新日期:2020-06-04
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