For complex systems involving multiple operating conditions and multiple failure modes, its reliability analysis usually presents the cascade failure correlation between multiple levels (i.e., operating condition level, failure mode level) and the strong coupling analysis between multiple physical fields (i.e., fluid, thermal, structure), leading to the traditional integral or separate reliability modeling methods prone to unacceptable computing efficiency or accuracy. In this study, to improve the computing performance of the multi-level reliability analysis, by fusing the cascade synchronous strategy (CSS) and wavelet neural network-based AdaBoost (WNN-Ada) ensemble learning, a cascade ensemble learning (CEL) method is proposed. The complex composite function approximation (including three composite levels and nine nonlinear sub-functions) and the multi-level reliability evaluation of aeroengine turbine rotor system (including three operating conditions and three failure modes) are served as the numerical and engineering experiments, to evaluate the effectiveness of the present cascade ensemble learning. The comparison investigation in two experiments reveals that the presented approach shows evident advantages in terms of computing accuracy as well as computing efficiency. The current efforts shed light on the development of reliability modeling for complex multi-level systems.

中文翻译：

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用于多级可靠性评估的级联集成学习

对于涉及多种工况、多种故障模式的复杂系统，其可靠性分析通常呈现多个层次（即工况级、故障模式级）之间的级联故障关联性和多个物理场（即流体、热力场）之间的强耦合分析。 、结构），导致传统的整体或分离的可靠性建模方法容易出现计算效率或精度不可接受的问题。在本研究中，为了提高多级可靠性分析的计算性能，通过融合级联同步策略（CSS）和基于小波神经网络的AdaBoost（WNN-Ada）集成学习，提出了一种级联集成学习（CEL）方法建议的。通过复杂的复合函数逼近（包括三个复合层次和九个非线性子函数）和航空发动机涡轮转子系统的多层次可靠性评估（包括三种工况和三种失效模式）作为数值和工程实验，评估当前级联集成学习的有效性。两个实验的对比研究表明，所提出的方法在计算精度和计算效率方面表现出明显的优势。当前的工作揭示了复杂多级系统可靠性建模的发展。