Analyzing component failures in series-parallel systems with dependent components

IF 6.7 1区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computers & Industrial Engineering Pub Date : 2024-09-27 DOI:10.1016/j.cie.2024.110604
Nuria Torrado , Murat Ozkut
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Abstract

This paper investigates a series-parallel system comprising N independent subsystems with interchangeable dependent components, a prevalent reliability structure in engineering and network design. The primary aim of this research is to derive the joint probability distribution of the number of failed components within these configurations, considering component dependence and varying distributions across subsystems. This approach reflects a more realistic scenario than previously explored in the literature. Initially, the analysis is conducted for systems with two subsystems and subsequently extended to encompass configurations with N subsystems. The study also evaluates key reliability metrics including the average number of failed components and the mean time to failure (MTTF) of the entire system, theoretically proving that the system’s MTTF increases with the number of components under certain sufficient conditions. In addition to probabilistic analysis, an optimization problem is addressed to determine the optimal allocation of components within each subsystem. The objective is to minimize the average cost associated with corrective maintenance, thereby enhancing the cost-effectiveness of system operation.
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分析具有从属组件的串并联系统中的组件故障
本文研究了由 N 个独立子系统组成的串并联系统,这些子系统具有可互换的从属组件,是工程和网络设计中普遍采用的可靠性结构。这项研究的主要目的是,在考虑到组件依赖性和各子系统间不同分布的情况下,推导出这些配置中故障组件数量的联合概率分布。与之前的文献相比,这种方法反映了一种更为现实的情况。分析最初针对两个子系统的系统进行,随后扩展到包含 N 个子系统的配置。研究还评估了关键的可靠性指标,包括故障组件的平均数量和整个系统的平均故障时间(MTTF),从理论上证明了在某些充分条件下,系统的 MTTF 会随着组件数量的增加而增加。除概率分析外,还需要解决一个优化问题,以确定每个子系统内组件的最佳分配。目标是最大限度地降低与纠正性维护相关的平均成本,从而提高系统运行的成本效益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computers & Industrial Engineering
Computers & Industrial Engineering 工程技术-工程:工业
CiteScore
12.70
自引率
12.70%
发文量
794
审稿时长
10.6 months
期刊介绍: Computers & Industrial Engineering (CAIE) is dedicated to researchers, educators, and practitioners in industrial engineering and related fields. Pioneering the integration of computers in research, education, and practice, industrial engineering has evolved to make computers and electronic communication integral to its domain. CAIE publishes original contributions focusing on the development of novel computerized methodologies to address industrial engineering problems. It also highlights the applications of these methodologies to issues within the broader industrial engineering and associated communities. The journal actively encourages submissions that push the boundaries of fundamental theories and concepts in industrial engineering techniques.
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