Aero-engine unbalanced fault location identification method based on deep learning

被引：0
作者：
Chen G. [1 ]
Yang M. [1 ]
Yu P. [1 ]
机构：
[1] College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing
来源：
Hangkong Dongli Xuebao/Journal of Aerospace Power | 2020年 / 35卷 / 12期
关键词：
Aero-engine rotor system; Casing test point; Deep convolution neural network; Fault location identification; Unbalance fault;
D O I：
10.13224/j.cnki.jasp.2020.12.014
中图分类号：
学科分类号：
摘要：
For the problem of aero-engine unbalanced fault location diagnosis based on casing test points, a method of aero-engine unbalanced fault location diagnosis based on deep convolution neural network was presented. The coupling dynamic model of a typical dual-rotor aero-engine was established, and the numerical integration method was used to realize the numerical simulation of unbalanced fault. Four unbalanced fault positions were selected from the high and low pressure rotors of the compressor end to the turbine end as the diagnostic object. A large number of unbalanced fault samples obtained by simulation were used to train the deep convolution neural network, and the excellent feature learning ability of the deep convolution neural network was used to realize the identification of different positions of the aeroengine unbalanced fault. The numerical experimental results fully showed the accuracy of the method to identify the unbalanced fault locations of aero-engine reached to 95%. © 2020, Editorial Department of Journal of Aerospace Power. All right reserved.
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页码：2602 / 2615
页数：13
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