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Availability evaluation and design optimization of multi-state weighted k-out-of-n systems

机译:多状态加权n-out-n系统的可用性评估和设计优化

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The multi-state weighted k-out-of-n system model can illustrate the performance and reliability/availability relationship between components and system so that attracted more and more attentions in recent years. Under the condition that the state probabilities of components are dynamically changing with time, this paper presents a generalized instantaneous availability model for multi-state weighted k-out-of-n systems and an efficient Genetic Algorithm (GA) is proposed to compute optimal reliability and performance design values for components. Firstly, a Universal Generating Function (UGF) approach is developed to efficiently determine the availability of system assuming the state probability distributions of multi-state components follow the non-homogeneous continuous time Markov process. And then, taking into consideration of availability and cost constraints respectively, two design optimization models are set up to obtain the optimal design solutions for component via improved GA. Finally, an example analysis of transportation systems in a naval shipyard is shown to demonstrate effectiveness of the proposed model. Our results show that the improved GA can enhance the applicability of design results and conclusions of our paper can provide clear policy recommendations for design optimization of multi-state weighted k-out-of-n system.
机译:多状态加权k-out-n-n系统模型可以说明组件与系统之间的性能以及可靠性/可用性关系,因此近年来引起了越来越多的关注。在组件状态概率随时间动态变化的情况下,提出了一种多状态加权k-out-n系统的广义瞬时可用性模型,并提出了一种有效的遗传算法来计算最优可靠性。和组件的性能设计值。首先,假设多状态分量的状态概率分布遵循非均匀连续时间马尔可夫过程,开发了通用生成函数(UGF)方法来有效地确定系统的可用性。然后,分别考虑可用性和成本约束,建立了两个设计优化模型,以通过改进的遗传算法获得组件的最佳设计解决方案。最后,对海军造船厂的运输系统进行了示例分析,结果证明了该模型的有效性。我们的结果表明,改进的遗传算法可以增强设计结果的适用性,并且本文的结论可以为多状态加权k-out-n系统的设计优化提供明确的政策建议。

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