IEEE Transactions on Reliability, 10 Nov 2023, ISSN: 0018-9529, DOI:

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

Chao Huang1, Siqi Bu1, Cheng-Wei Fei2, Namkyoung Lee3, and Shu Wa Kong1
1 Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong Centre for Advances in Reliability and Safety (CAiRS), Hong Kong, China
2 Department of Aeronautics and Astronautics, Fudan University, Shanghai, China
3 Center for Advanced Life Cycle Engineering, University of Maryland, College Park, MD, USA


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.

This article is available online here and to CALCE Consortium Members for personal review.

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