可解释性
可靠性(半导体)
概率逻辑
可靠性工程
人工神经网络
约束(计算机辅助设计)
计算机科学
替代模型
机器学习
工程类
人工智能
机械工程
功率(物理)
物理
量子力学
作者
Chao Huang,Siqi Bu,Cheng-Wei Fei,Namkyoung Lee,Siu-kuen Sammy Kong
出处
期刊:IEEE Transactions on Reliability
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:: 1-15
标识
DOI:10.1109/tr.2023.3324896
摘要
Aeroengine blisks operate in a harsh working environment and are prone to low cycle fatigue (LCF) failure. The probabilistic LCF life prediction considering multiple uncertainties needs to be performed for reliability assessment. To consider the combined effects of heterogeneous uncertainties, this article employs a unified reliability assessment method by processing the uncertainties simultaneously. To overcome the extremely time-consuming limitation of probabilistic finite-element model simulation, this article develops an ensemble generalized constraint neural network (EGCNN)-based unified reliability assessment method. The developed EGCNN surrogate model can conduct efficient, accurate, interpretable, and robust reliability assessments with nonlinear fitting capability, knowledge interpretability, and premature avoidance ability. The developed EGCNN-based unified reliability assessment method can also be applied to other assets and failure mechanisms, providing a new reliability-based design optimization tool.
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