首页> 外文会议>International FLINS conference on intelligent techniques and soft computing in nuclear science and engineering >OPTIMIZATION OF THE DEVICE OF STAGES THROUGH GENETIC ALGORITHMS FOR NON-MARKOVIAN SYSTEMS RELIABILITY EVALUATION: AN APPLICATION TO NUCLEAR SAFETY SYSTEMS
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OPTIMIZATION OF THE DEVICE OF STAGES THROUGH GENETIC ALGORITHMS FOR NON-MARKOVIAN SYSTEMS RELIABILITY EVALUATION: AN APPLICATION TO NUCLEAR SAFETY SYSTEMS

机译:非马基系统可靠性评估的遗传算法优化阶段装置:核安全系统的应用

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When a safety system is under aging effects, failures times follow non-exponential distributions, and the transition rates become time-dependent. The stochastic process employed in the modeling becomes Non-markovian. In this paper, this analysis is developed using an alternative method, the device of stages, which allows the transformation of Non-markovian models into equivalentMarkovian ones. The transformation consists in reshaping the initial state transition diagram into a new one where fictitious states (stages) are added and whose transition rate are constant. The number of added stages and their connections are identification parameters of the stage configuration used for the equivalentMarkovian model and depend on the initial transitions rate. Overcoming the aforementioned difficulties is a complex optimization problem. In order to perform a global search in such a topologically complex space, a genetic algorithm has been developed to automatically determine the combination of stages and the set of parameters which better represent the analyzed distribution. The developed genetic algorithm has demonstrated a good ability for optimizing the method of stages. Results concerning initial applications to nuclear safety system pumps are shown and discussed, for which Weibull and log-normal distributions are employed for modeling the failure times.
机译:当安全系统处于老化效果下,故障时间遵循非指数分布,并且过渡率变为时间。模型中使用的随机过程变为非马尔可夫。在本文中,使用替代方法,阶段设备开发了该分析,其允许将非马尔可维亚模型转换为同等标记的。转换包括将初始状态转换图重塑成新的态度(阶段)的新一个,并且其过渡率是恒定的。添加阶段的数量及其连接是用于等效标记模型的阶段配置的识别参数,并取决于初始转换速率。克服上述困难是复杂的优化问题。为了在这种拓扑复杂的空间中执行全球搜索,已经开发了一种遗传算法以自动确定阶段的组合和更好地代表分析分布的参数集。发达的遗传算法表明了优化阶段方法的良好能力。关于核安全系统泵的初始应用的结果显示和讨论,用于模拟故障时间的Weibull和Log-Normal分布。

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