电子测量与仪器学报

毛向向,王红军,韩凤霞,王楠,陈晓,杨伟.基于深度卷积神经网络的机电系统故障分类识别方法[J].电子测量与仪器学报,2021,35(2):87-93

基于深度卷积神经网络的机电系统故障分类识别方法

Fault classification and recognition of electromechanical system based on deep convolutional neural network

DOI：

英文关键词:electromechanical system deep learning CNN fault diagnosis

基金项目:国家自然科学基金(51975058)项目资助

作者	单位
毛向向	1北京信息科技大学机电工程学院北京100192；2北京市高端装备智能感知与控制国际科技合作基地北京100192；
王红军	1北京信息科技大学机电工程学院北京100192；2北京市高端装备智能感知与控制国际科技合作基地北京100192；3机电系统测控北京市重点实验室北京100192
韩凤霞	1北京信息科技大学机电工程学院北京100192；2北京市高端装备智能感知与控制国际科技合作基地北京100192；
王楠	1北京信息科技大学机电工程学院北京100192；2北京市高端装备智能感知与控制国际科技合作基地北京100192；
陈晓	1北京信息科技大学机电工程学院北京100192
杨伟	1北京信息科技大学机电工程学院北京100192；2北京市高端装备智能感知与控制国际科技合作基地北京100192；

Author	Institution
Mao Xiangxiang	1School of Mechanical and Electrical Engineering, Beijing Information Science and Technology University, Beijing 100192,China; 2Beijing International Science Joint Base on Highend Equipment Intelligent Perception and Control (BISTU),Beijing 100192, China;
Wang Hongjun	1School of Mechanical and Electrical Engineering, Beijing Information Science and Technology University, Beijing 100192,China; 2Beijing International Science Joint Base on Highend Equipment Intelligent Perception and Control (BISTU),Beijing 100192, China; 3Beijing Key Lab of Mechatronic System Measure and Control (BISTU), Beijing 100192, China
Han Fengxia	1School of Mechanical and Electrical Engineering, Beijing Information Science and Technology University, Beijing 100192,China; 2Beijing International Science Joint Base on Highend Equipment Intelligent Perception and Control (BISTU),Beijing 100192, China;
Wang Nan	1School of Mechanical and Electrical Engineering, Beijing Information Science and Technology University, Beijing 100192,China; 2Beijing International Science Joint Base on Highend Equipment Intelligent Perception and Control (BISTU),Beijing 100192, China;
Chen Xiao	1School of Mechanical and Electrical Engineering, Beijing Information Science and Technology University, Beijing 100192,China;
Yang Wei	1School of Mechanical and Electrical Engineering, Beijing Information Science and Technology University, Beijing 100192,China; 2Beijing International Science Joint Base on Highend Equipment Intelligent Perception and Control (BISTU),Beijing 100192, China;

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中文摘要:

随着高端装备在工业领域的广泛应用，其运行状态对装备的安全性和产品的性能影响重大，突发故障往往造成巨大的人民生命财产的巨大损失并影响社会的安全稳定。机电系统多处于变转速工作状态，其状态特征信息难以获取，为机电系统的故障诊断和预测带来困难。针对此问题，提出了深度学习的机电系统故障分类识别诊断模型。首先将采集到的关键部位的振动信号进行时频变换转换为时频图构成输入样本；其次将样本输入深度学习神经网络进行特征学习和状态识别；研究了不同变换与深度学习卷积神经网络相结合的方法，应用于某机电系统试验台进行故障状态分类性能对比，实验结果表明该方法为机电系统的故障诊断提供了一种新途径。

英文摘要:

With the wide application of high end equipment in the industrial field, its operating state has a great impact on the safety of equipment and the performance of products, sudden failures often cause huge loss of people's lives and property and affect the safety and stability of society. The electromechanical system is in the state of variable speed operation, and its state characteristic information is difficult to obtain, which makes it difficult to diagnose and predict the fault of the electromechanical system. In view of this problem, a fault classification, recognition and diagnosis model of electromechanical system based on deep learning is proposed. Firstly, the vibration signals of the key parts are converted into time frequency graphs by time frequency transformation to form the input samples；Secondly, the samples were input into the deep learning neural network for feature learning and state recognition, the method of combining different transformations and deep learning convolutional neural networks is studied, which is applied to a mechanical and electrical system test bench to compare the fault state classification performance. The experimental results show that this method provides a new way for the fault diagnosis of electromechanical system.

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