Highly imbalanced fault diagnosis of turbine blade cracks via deep focal dynamically weighted conditional variational auto-encoder network

被引:8
作者
Huang, Xin [1 ]
Zhang, Xiaodong [1 ,2 ]
Xiong, Yiwei [1 ]
Fan, Bochao [1 ]
Dai, Fei [1 ]
机构
[1] Xi An Jiao Tong Univ, Sch Mech Engn, Xian 710049, Peoples R China
[2] Xi An Jiao Tong Univ, Key Lab, Educ Minist Modern Design & Rotor Bearing Syst, Xian 710049, Peoples R China
基金
中国国家自然科学基金;
关键词
Blade crack diagnosis; Imbalanced class samples; Conditional variational autoencoder; Deep focal loss; Three-dimensional blade tip clearance; DATA AUGMENTATION;
D O I
10.1016/j.aei.2024.102612
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Turbine blade cracks pose a critical threat to the safety of aircraft in flight. However, data collected from turbine blades often exhibit substantial imbalances in real industry settings, posing challenges for effective blade crack diagnosis using intelligent models. To solve this problem, our study presents an intelligent fault diagnosis framework utilizing three-dimensional blade tip clearance (3D-BTC) signal under highly imbalanced samples, the so-called deep focal dynamically weighted conditional variational auto -encoder (DWCVAE-DFL), which can leverage the benefits of cost -sensitive learning methods and deep generative models to effectively tackles the diagnosis complexity of imbalanced class samples, thereby improving the blade crack diagnosis performance. Specifically, the novel deep focal loss (DFL) function is first designed to consider the varying sensitivity of imbalanced class samples in feature extraction and fault classification. The blade -rotor simulation rig is used for validating the effectiveness of our proposed method. Our experimental results indicate that the performance of our proposed method outperforms the latest methods when dealing with highly imbalanced samples.
引用
收藏
页数:15
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