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Safety analysis for the posfust reliability model under possibilistic input and fuzzy state

机译:可能输入和模糊状态下期望可靠性模型的安全性分析

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摘要

The structural input can be simply classified as probabilistic one and possibilistic one, and the structural state can be divided into binary state and fuzzy state. Combining different kinds of structural input and state, the reliability analysis model can be sorted into four types, i.e., the one based on probabilistic input and binary state (probist), the one based on probabilistic input and fuzzy state (profust), the one based on possibilistic input and binary state (posbist) and the one based on possibilistic input and fuzzy state (posfust). Various researches about the former three reliability models have been developed, whereas the study on the posfust reliability model is rare. Due to this fact, the fuzzy failure credibility is established to measure the safety degree of the posfust model, and it is a fuzzy safety index defined by the definite integral of the failure membership function. Then, to estimate the defined fuzzy failure credibility efficiently, two adaptive Kriging based methods are proposed. The first one is based on the definition of the fuzzy failure credibility, in which the lower and upper bounds of performance function are mainly concerned by the strategy of constructing the Kriging model. The second one combines the adaptive Kriging and fuzzy simulation algorithm, and it is based on the relationship between the defined fuzzy failure credibility and the failure credibility in the posbist reliability model. Several examples are provided to verify the rationality of the established fuzzy failure credibility, and the efficiency and accuracy of the proposed methods. (C) 2020 Elsevier Masson SAS. All rights reserved.
机译:结构输入可以简单地分为概率一和可能性一,结构状态可以分为二元状态和模糊状态。结合不同类型的结构输入和状态,可靠性分析模型可以分为四种类型,一种是基于概率输入和二进制状态(概率),一种是基于概率输入和模糊状态(概率)。一种基于可能的输入和二元状态(posbist),另一种基于可能的输入和模糊状态(posfust)。已经对前三种可靠性模型进行了各种研究,而对后可靠性模型的研究却很少。由于这个事实,建立了模糊失效可信度来度量后继模型的安全度,并且它是由失效隶属度函数的确定积分定义的模糊安全指数。然后,为了有效地估计定义的模糊失效可信度,提出了两种基于自适应克里格的方法。第一个是基于模糊失效可信度的定义,其中性能函数的上下限主要与构造克里格模型的策略有关。第二种方法是将自适应Kriging和模糊仿真算法结合在一起,它是基于概率论可靠性模型中定义的模糊失效可信度和失效可信度之间的关系。提供了几个例子来验证所建立的模糊失效可信度的合理性,以及所提出方法的效率和准确性。 (C)2020 Elsevier Masson SAS。版权所有。

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