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A framework for conducting mechanistic based reliability assessments of components operating in complex systems.

机译：用于对复杂系统中运行的组件进行基于机械的可靠性评估的框架。

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Reliability prediction of components operating in complex systems has historically been conducted in a statistically isolated manner. Current physics-based, i.e. mechanistic, component reliability approaches focus more on component-specific attributes and mathematical algorithms and not enough on the influence of the system. The result is that significant error can be introduced into the component reliability assessment process.; The objective of this study is the development of a framework that infuses the needs and influence of the system into the process of conducting mechanistic-based component reliability assessments. The formulated framework consists of six primary steps. The first three steps, identification, decomposition, and synthesis, are primarily qualitative in nature and employ system reliability and safety engineering principles to construct an appropriate starting point for the component reliability assessment.; The following two steps are the most unique. They involve a step to efficiently characterize and quantify the system-driven local parameter space and a subsequent step using this information to guide the reduction of the component parameter space. The local statistical space quantification step is accomplished using two proposed multivariate probability models: Multi-Response First Order Second Moment and Taylor-Based Inverse Transformation. Where existing joint probability models require preliminary distribution and correlation information of the responses, these models combine statistical information of the input parameters with an efficient sampling of the response analyses to produce the multi-response joint probability distribution.; Parameter space reduction is accomplished using Approximate Canonical Correlation Analysis (ACCA) employed as a multi-response screening technique. The novelty of this approach is that each individual local parameter and even subsets of parameters representing entire contributing analyses can now be rank ordered with respect to their contribution to not just one response, but the entire vector of component responses simultaneously.; The final step of the framework is the actual probabilistic assessment of the component. Although the same multivariate probability tools employed in the characterization step can be used for the component probability assessment, variations of this final step are given to allow for the utilization of existing probabilistic methods such as response surface Monte Carlo and Fast Probability Integration.; The overall framework developed in this study is implemented to assess the finite-element based reliability prediction of a gas turbine airfoil involving several failure responses. Results of this implementation are compared to results generated using the conventional 'isolated' approach as well as a validation approach conducted through large sample Monte Carlo simulations. The framework resulted in a considerable improvement to the accuracy of the part reliability assessment and an improved understanding of the component failure behavior. Considerable statistical complexity in the form of joint non-normal behavior was found and accounted for using the framework. Future applications of the framework elements are discussed.
机译：历史上，以统计上孤立的方式对在复杂系统中运行的组件进行可靠性预测。当前基于物理的，即机械的组件可靠性方法更多地关注于特定于组件的属性和数学算法，而不足以关注系统的影响。结果是，可以将严重错误引入组件可靠性评估过程中。这项研究的目的是开发一个框架，该框架将系统的需求和影响纳入进行基于机械的组件可靠性评估的过程中。制定的框架包括六个主要步骤。前三个步骤（识别，分解和综合）本质上主要是定性的，并采用系统可靠性和安全工程原理来构建组件可靠性评估的适当起点。以下两个步骤是最独特的。它们涉及一个有效地表征和量化系统驱动的局部参数空间的步骤，以及一个使用此信息指导减少组件参数空间的步骤。局部统计空间量化步骤是使用两个建议的多元概率模型完成的：多响应一阶第二矩和基于泰勒的逆变换。在现有的联合概率模型需要响应的初步分布和相关信息的情况下，这些模型将输入参数的统计信息与响应分析的有效采样结合起来，以产生多响应联合概率分布。使用近似规范相关分析（ACCA）作为多响应筛选技术可完成参数空间的缩减。这种方法的新颖性在于，现在，可以将代表整个贡献分析的每个单独的局部参数，甚至是参数的子集，不仅考虑到它们对一个响应的贡献，而且还对组件响应的整个向量的贡献进行排序。框架的最后一步是对该组件的实际概率评估。尽管在特征化步骤中可以使用相同的多元概率工具进行分量概率评估，但给出了最后一步的变体，以允许利用现有的概率方法，例如响应面蒙特卡罗和快速概率积分。本研究开发的总体框架用于评估涉及多个故障响应的燃气轮机翼型基于有限元的可靠性预测。将该实施结果与使用常规“隔离”方法以及通过大样本蒙特卡洛模拟进行的验证方法生成的结果进行比较。该框架极大地提高了零件可靠性评估的准确性，并增强了对零件故障行为的理解。发现并以联合非正常行为的形式出现了相当大的统计复杂性，并且使用了该框架。讨论了框架元素的未来应用。

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