The cascading reliability evaluation of multi-failure modes of complex system/structure usually needs to repeatedly establish mathematical models with the step-by-step modeling strategy, which weakens the correlation between multi-failure modes. To improve the efficiency and precision of cascading reliability evaluation, a hybrid adaptive coordination surrogate modeling-based improved moving regression (IMR-HACSM, short for) method is proposed based on hybrid adaptive coordination surrogate modeling (HACSM) and improved moving regression (IMR) technique. In this proposed method, the HACSM is developed from coordinative modeling idea, adaptive selection strategy and surrogate model, and the IMR technique is expanded by artificial protozoa optimizer (APO) algorithm and moving least square (MLS) method. Herein, the coordinative modeling idea is used to collaboratively establish these models of associated failures, the adaptive selection strategy is utilized to choose suitable forms of mathematical models of cascading failure and associated failures, the surrogate model is treated as the basis functions of top-objective and sub-objectives, the APO algorithm is employed to search the optimal radius of compact support region and to ensure effective modeling samples, and the MLS method is adopted to resolve these unknown coefficients of mathematical models. Besides, three examples are used to verify the effectiveness of the proposed method, including a two-level nested function approximation, an aircraft landing gear brake system temperature difference reliability assessment and an aeroengine turbine blisk low cycle fatigue (LCF) life reliability analysis. The results show that the IMR-HACSM method holds excellent modeling features and simulation performance relative to some existing different methods, including response surface method (RSM), Kriging, support vector machine (SVM), artificial neural network (ANN), RSM-based moving regression (MR-RSM), Kriging-based moving regression (MR-K), SVM-based moving regression (MR-SVM), ANN-based moving regression (MR-ANN), RSM-based IMR (IMR-RSM), Kriging-based IMR (IMR-K), SVM-based IMR (IMR-SVM) and ANN-based IMR (IMR-ANN). The efforts of this work provide useful ways for the cascading reliability evaluation of complex system/structure, and contribute to the design of high reliability products.

中文翻译：

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IMR-HACSM：基于混合自适应协调代理建模的改进移动回归方法，用于级联可靠性评估


复杂系统/结构的多失效模式的级联可靠性评估通常需要以逐步建模策略重复建立数学模型，弱化了多失效模式之间的相关性。为提高级联可靠性评价的效率和精度，提出了一种基于混合自适应协调代理建模（HACSM）和改进移动回归（IMR）技术的基于混合自适应协调代理建模的改进移动回归（IMR-HACSM，简称）方法。该方法从协调建模思想、自适应选择策略和代理模型发展来HACSM，并通过人工原生动物优化器（APO）算法和移动最小二乘法（MLS）方法扩展了IMR技术。本文采用协调建模思想协同建立关联失效模型，采用自适应选择策略选择合适的级联失效和相关失效数学模型形式，将代理模型作为顶部目标和子目标的基础函数，采用 APO 算法搜索紧致支撑区域的最优半径并确保有效的建模样本， 并采用 MLS 方法求解数学模型的这些未知系数。此外，还使用了三个算例来验证所提方法的有效性，包括两级嵌套函数近似、飞机起落架制动系统温差可靠性评估和航空发动机涡轮叶盘低周疲劳 （LCF） 寿命可靠性分析。 结果表明，相对于现有的一些不同方法，包括响应面法 （RSM）、克里金法、支持向量机 （SVM）、人工神经网络 （ANN）、基于 RSM 的移动回归 （MR-RSM）、基于克里金法的移动回归 （MR-K）、基于 SVM 的移动回归 （MR-SVM）、基于 ANN 的移动回归 （MR-ANN）、RSM 的 IMR （IMR-RSM）、基于 Kriging 的 IMR （IMR-K）、 基于 SVM 的 IMR （IMR-SVM） 和基于 ANN 的 IMR （IMR-ANN）。这项工作为复杂系统/结构的级联可靠性评估提供了有用的方法，并为高可靠性产品的设计做出了贡献。