Aero-engine Fault Diagnosis Based on Deep Self-Coding Network

被引：0

作者：

Cui J. ^{[1
]}

Li G. ^{[1
]}

Jiang L. ^{[1
]}

Yu M. ^{[1
]}

Wang J. ^{[2
]}

机构：

[1] School of Automation, Shenyang Aerospace University, Shenyang

[2] Aviation Key Laboratory of Science and Technology on Fault Diagnosis and Health Management, Shanghai

来源：

| 1600年 / Nanjing University of Aeronautics an Astronautics卷 / 41期

关键词：

Aero engine; Deep self-encoding network; Fault diagnosis; Neural network;

D O I：

10.16450/j.cnki.issn.1004-6801.2021.01.012

中图分类号：

学科分类号：

摘要：

The fault diagnosis of an aero-engine always plagues the industry due to its complex internal structure. In light of this problem, this paper proposes a fault diagnosis method based on the deep self-coding network. First, the monitoring data is preprocessed, and the structure of the network is constructed according to the data's features. Then, the unlabeled data samples train the network for the initial values of its parameters, and the labeled samples work for a slight adjustment. Thus, the aeroengine fault diagnosis model based on deep self-encoding neural network is established. Finally, the proposed model presents its advantages over common fault diagnosis methods, including back propagation neural network and radial basis neural network, in accuracy according to the tested of labeled samples. © 2021, Editorial Department of JVMD. All right reserved.

引用

页码：85 / 89

页数：4

共 16 条

[1]

WU Y H, SHAN M X, QIAN Y N, Et al., Aero engine rub-impact fault diagnosis based on wavelet packet transform and the local discriminate bases, Applied Mechanics and Materials, 226, pp. 740-744, (2012)

[2]

QIAN Bin, TANG Zhenmin, XU Wei, Pavement crack detection based on sparse self-coding, Journal of Beijing Institute of Technology, 35, 8, pp. 800-804, (2015)

[3]

LI Tao, CAO Hui, GUO Lele, Speech depth feature extraction method for deep neural networks, Journal of Acoustics, 37, 4, pp. 73-77, (2018)

[4]

SHI Xin, ZHU Yongli, NING Xiaoguang, Et al., Fault diagnosis of power transformers based on deep self-coded network, Electric Power Automation Equipment, 36, 5, pp. 122-126, (2016)

[5]

(2017)

[6]

WU Qi, CHU Yinxue, CHEN Xi, Et al., Pilot fatigue state recognition based on deep contraction sparse self-coding network, Control and Decision, 33, 12, pp. 154-160, (2018)

[7]

YUAN Wenjun, LIU Fei, WANG Xiaofeng, Et al., Bearing fault diagnosis based on deep self-coded network, Noise and Vibration Control, 38, 5, pp. 214-220, (2018)

[8]

(2014)

[9]

CHI Xiaoguang, HE Jin, CAO Meng, Et al., Partial discharge pattern recognition based on depth sparse noise reduction self-coded network, Electrical Automation, 40, 238, pp. 119-122, (2018)

[10]

CUI Guangxin, LI Diankui, Overview of deep learning based on self-encoding algorithm, Computer Systems Applications, 9, pp. 47-51, (2018)

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