Redundancy optimization of cold-standby systems under periodic inspection and maintenance. (December 2018)

• Record Type: Journal Article
• Authors: Wang, Wei; Wu, Zhiying; Xiong, Junlin; Xu, Yaofeng
• Is Part Of: Reliability engineering & system safety. Volume 180(2018)
• Page Start: 394
• Page End: 402
• Publication Date: 2018-12
• Subjects: Cold-standby -- Genetic algorithm -- Performance degradation -- Periodic inspection -- Preventive maintenance -- Redundancy allocation -- System reliability; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2018.08.004
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: We investigate a redundancy allocation problem for a cold-standby system with degrading components. We consider performing periodic inspection and preventive maintenance for the degrading cold-standby components. The proposed optimization problem has three parts of decision variables: component choices, the number of redundant components, and the length of periodic inspection interval. We use approximation methods when evaluating the system reliability (objective function) and expected cost. A modified genetic algorithm involving dual mutation and random keys technique is developed to solve the proposed problem. Abstract: In this paper, a redundancy allocation problem for cold-standby systems with degrading components is studied. We consider performing a policy of periodic inspection and preventive maintenance on the cold-standby components. Components in cold-standby mode are assumed to suffer from performance degradation when exposed in extreme standby environments for a long-term storage. Except the online working components, other cold-standby components are periodically inspected and preventively maintained to retain them in or restore them to an acceptable condition. The situation, that the cold-standby components are restored like new but their failure rates increase, is considered. In this paper, we develop a model to determine an optimal combination of the component choices, the number of redundant components, and the length of periodic inspection interval, so as to maximize the system reliability while meeting the resource limits, such as cost and weight. As it is difficult to obtain a closed-form expression of the objective function for the optimization problem, an approximated objective function is proposed. The approximation is also used for evaluating the expected cost of periodic inspection and maintenance. We then develop a modified genetic algorithm to solve the optimization problem. Finally, a numerical example is presented to illustrate the effectiveness of the proposed model and the solution methodology.