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首页> 外文期刊>Reliability Engineering & System Safety >Redundancy allocation in series-parallel systems under warm standby and active components in repairable subsystems
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Redundancy allocation in series-parallel systems under warm standby and active components in repairable subsystems

机译：热备用和可修复子系统中活动组件下的串并联系统中的冗余分配

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摘要

Redundancy allocation is one of the most common approaches to increase the system reliability. In this study, a new model is developed to maximize mean time to failure and to minimize the cost of a system. In general, many researchers are now considering the active redundancy even more than before; however, it is possible for a particular system design to utilize active redundancy and warm-standby redundancy as well. In this model, each subsystem can use both active and warm-standby strategies simultaneously. Moreover, the model allows for component mixing such that components of different types may be used in each subsystem. Thus, the aim of the proposed model is to select the best redundancy strategy, components' types and levels of redundancy for each subsystem. The simulation and neural network methods are applied considering the structural complexity of the model and repairable components. In order to solve the problem, meta-heuristic of Multi Objective Water Flow algorithm (MOWFA) is proposed and compared to NSGA-II and NRGA. Also, for tuning the meta-heuristics parameters, the Taguchi design of experiments is employed. The algorithms are used to solve 32 test problems and the results are compared. Finally, the results are analyzed and discussed.
机译：冗余分配是提高系统可靠性的最常用方法之一。在这项研究中，开发了一种新模型以最大化平均故障时间并最小化系统成本。总的来说，许多研究人员现在比以前更多地考虑主动冗余。但是，特定的系统设计也可以利用主动冗余和热备份冗余。在此模型中，每个子系统可以同时使用主动策略和热备份策略。而且，该模型允许组分混合，使得可以在每个子系统中使用不同类型的组分。因此，提出的模型的目的是为每个子系统选择最佳的冗余策略，组件的类型和冗余级别。考虑到模型和可修复组件的结构复杂性，应用了仿真和神经网络方法。为了解决该问题，提出了多目标水流算法（MOWFA）的元启发式方法，并将其与NSGA-II和NRGA进行了比较。同样，为了调整元启发式参数，采用了Taguchi设计的实验。该算法用于解决32个测试问题，并比较了结果。最后，对结果进行了分析和讨论。

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