Reliability Assessment for Aeroengine Blisks Under Low Cycle Fatigue With Ensemble Generalized Constraint Neural Network

• Published in: IEEE transactions on reliability 2024-06, Vol.73 (2), p.922-936
• Main Authors: Huang, Chao, Bu, Siqi, Fei, Cheng-Wei, Lee, Namkyoung, Kong, Shu Wa
• Format: Article
• Language: English
• Subjects: Aerospace engines; Analytical models; Blisks; Constraint handling; Design optimization; Failure mechanisms; Fatigue; Fatigue failure; Finite element method; Generalized constraint neural network (GCNN); interpretable machine learning; Life prediction; Low cycle fatigue; Neural networks; Probabilistic logic; Reliability analysis; Reliability engineering; structural reliability assessment; surrogate model; Uncertainty; Working conditions
• ISSN: 0018-9529; 1558-1721
• DOI: 10.1109/TR.2023.3324896

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.