Aero-engine Sensor Fault Diagnosis Based on Convolutional Neural Network

被引：0

作者：

Liu Weimin ^{[1
]}

Hu Zhongzhi ^{[1
]}

机构：

[1] Nanjing Univ Aeronaut & Astronaut, Coll Energy & Power Engn, Jiangsu Prov Key Lab Aerosp Power Syst, Nanjing 210016, Peoples R China

来源：

PROCEEDINGS OF THE 2019 31ST CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2019) | 2019年

关键词：

Aero-engine; Sensor fault diagnosis; Convolutional neural network; Deep learning;

D O I：

10.1109/ccdc.2019.8832487

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A fault diagnosis method based on convolutional neural network (CNN) is proposed for aero-engine sensors. Convolutional layers and pooling layers in CNN are used to extract correlation features among sensed signals, based on which fully connected layers are used to diagnose sensor faults. The inception module method is used to extract features on different data sizes among sensors, making the method capable of detecting multi-sensor faults. The simulation results based on a turbofan engine model demonstrate the feasibility and effectiveness of this method.

引用

页码：3314 / 3319

页数：6

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