Deep anomaly detection method for low-rotating speed rolling bearing faults of aero-engine

被引：0

作者：

Kang Y. ^{[1
]}

Chen G. ^{[2
]}

Sheng J. ^{[1
]}

Wang H. ^{[3
]}

Wei X. ^{[3
]}

机构：

[1] College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing

[2] College of General Aviation and Flight, Nanjing University of Aeronautics and Astronautics, Liyang

[3] Beijing Aeronautical Engineering Technology Research Center, Beijing

来源：

Zhendong yu Chongji/Journal of Vibration and Shock | 2024年 / 43卷 / 07期

关键词：

aero-engine; deep anomaly detection; low rotating speed; rolling bearing; Transformer;

D O I：

10.13465/j.cnki.jvs.2024.07.020

中图分类号：

学科分类号：

摘要：

Here, aiming at the problem of aero-engine rolling bearing faults at low rotating speed being difficult to detect, a deep support vector description method based on Transformer framework was proposed to detect faults of low rotating speed rolling bearings. Firstly, a vibration feature extraction backbone network based on Transformer model was constructed. Then, the extracted features were input into a 3-layer autoencoder structure to calculate the loss function of network model. In order to reduce network computation amount and improve training speed, time-domain vibration acceleration signals of rolling bearing were pre-processed and the frequency spectrum results obtained using fast Fourier transform (FFT) were taken as the input of network to complete training of model only using normal data. Finally, test verifications were performed on rotor tester of an aero-engine with casing, and a real aero-engine of a certain type, respectively. The results showed that the proposed method can correctly detect faults in low rotating speed rolling bearing with detection accuracies of 93% and 100%, respectively; the proposed method can have excellent anomaly detection ability and application value. © 2024 Chinese Vibration Engineering Society. All rights reserved.

引用

页码：186 / 195

页数：9

共 15 条

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