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Bayesian Reliability Estimation for Deteriorating Systems with Limited Samples Using the Maximum Entropy Approach

机译:使用最大熵方法的有限样本退化系统的贝叶斯可靠性估计

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In this paper the combinations of maximum entropy method and Bayesian inference for reliability assessment of deteriorating system is proposed. Due to various uncertainties, less data and incomplete information, system parameters usually cannot be determined precisely. These uncertainty parameters can be modeled by fuzzy sets theory and the Bayesian inference which have been proved to be useful for deteriorating systems under small sample sizes. The maximum entropy approach can be used to calculate the maximum entropy density function of uncertainty parameters more accurately for it does not need any additional information and assumptions. Finally, two optimization models are presented which can be used to determine the lower and upper bounds of systems probability of failure under vague environment conditions. Two numerical examples are investigated to demonstrate the proposed method.
机译:提出了结合最大熵方法和贝叶斯推理对劣化系统进行可靠性评估的方法。由于各种不确定性,较少的数据和不完整的信息,通常无法精确确定系统参数。这些不确定性参数可以通过模糊集理论和贝叶斯推论进行建模,事实证明,这些不确定性参数对于小样本量下恶化的系统很有用。最大熵方法可以用于更准确地计算不确定性参数的最大熵密度函数,因为它不需要任何其他信息和假设。最后,提出了两种优化模型,可用于确定模糊环境条件下系统故障概率的上限和下限。研究了两个数值示例,以证明所提出的方法。

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