A Gas Path Fault Diagnosis Method for Aero-engine Based on TCN-LGBM Model

被引：0
|
作者：
Lü W. [1 ]
Sun C. [1 ]
Ren L. [1 ]
Zhao J. [1 ]
Li Y. [1 ]
机构：
[1] Naval Aviation University, Shandong, Yantai
来源：
Binggong Xuebao/Acta Armamentarii | 2024年 / 45卷 / 01期
关键词：
aero-engine; attention mechanism; fault diagnosis; light gradient boosting machine; temporal convolutional network;
D O I：
10.12382/bgxb.2022.0615
中图分类号：
学科分类号：
摘要：
With the obvious characteristics of poor temporal logic in fault diagnosis and the strongly coupled feature parameters, the aero-engines working in the hostile gas path conditions of high temperature, pressure and strong vibration face with the degradation performance and structure defect problems such as fatigue and corrosion. And an aero-engine gas path fault diagnosis method based on temporal convolutional networks (TCN) and light gradient boosting machine (LGBM) is proposed to provide a feasible solution to the problems above. The diagnosis process can be divided into feature extraction and classification: TCN is introduced to guarantee the fault diagnosis training temporal logic and achieve the features fusion of distant layers and current layers, which is also strengthened by channel attention mechanism; the features are quickly classified based on LGBM model, and the Bayesian method is used to quickly optimize the model hyperparameters. Based on the aero-engine performance modelled by PROOSIS software, six types of fault mode are diagnosed and identified by taking a military low-bypass ratio turbofan engine as an example. The results indicate that the proposed model is effective for fault diagnosis and shows the superiority by comparing with other models. © 2024 China Ordnance Industry Corporation. All rights reserved.
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页码：253 / 263
页数：10
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