Fault diagnosis of aero-engine inter-shaft bearing based on Deep-GBM

被引：0
|
作者：
Tian J. [1 ]
Li Y. [1 ,2 ,3 ]
Ai Y. [1 ]
机构：
[1] Liaoning Province Key Laboratory of Advanced Measurement and Test Technology for Aviation Propulsion System, Shenyang Aerospace University, Shenyang
[2] Institute of Information Science, Beijing Jiaotong University, Beijing
[3] Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing Jiaotong University, Beijing
来源：
Hangkong Dongli Xuebao/Journal of Aerospace Power | 2019年 / 34卷 / 04期
关键词：
Deep gradent boosting model(Deep-GBM); Fault diagnosis; Inter-shaft bearing; Machine learning; Sample entropy;
D O I：
10.13224/j.cnki.jasp.2019.04.003
中图分类号：
学科分类号：
摘要：
In view of the difficulty in identifying the fault signal of the inter-shaft bearing of the aero-engine, a deep gradient boasting model (Deep-GBM) was proposed to improve the precision score by learning the feature of vibration signal step by step. Fault simulation experiment was conducted on a type of aeroengine intershaft bearing. Vibration fault signal was decomposed through empirical mode decomposition (EMD) method, and intrinsin mode function (IMF) component sample entropy of nonlinear dynamics parameters was collected as base features. With the model proposed, the aero-engine inter-shaft bearing was diagnosed respectively with fault in inner ring, comprehensive fault in inner ring and rolling element, and in normal condition, with stick-spalled fault and stick-scratched fault. The experimental results showed that the fault diagnosis accuracy of the Deep-GBM reached 87%, 28% higher than that of the traditional machine learning model. Besides, the model has been proved to have good generalization ability. © 2019, Editorial Department of Journal of Aerospace Power. All right reserved.
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页码：756 / 763
页数：7
相关论文
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