基于优化CNN与信息融合的地铁牵引电机轴承故障诊断  

Fault Diagnosis of Metro Traction Motor Bearing Based on Optimized CNN and Information Fusion

作　　者：王浏洋 徐彦伟[1,2] 颉潭成 曹胜博[1] WANG Liuyang;XU Yanwei;XIE Tancheng;CAO Shengbo(School of Mechatronics Engineering,Henan University of Science and Technology,Luoyang 471003,China;Henan Engineering Laboratory of Intelligent Numerical Control Equipment,Luoyang 471003,China)

机构地区：[1]河南科技大学机电工程学院,河南洛阳471003 [2]智能数控装备河南省工程实验室,河南洛阳471003

出　　处：《机械与电子》2023年第8期39-44,48,共7页Machinery & Electronics

基　　金：国家自然科学基金资助项目(51805151);河南省高等学校重点科研项目(21B460004)。

摘　　要：针对在单一传感器下轴承故障识别率低的问题,提出一种基于优化CNN与信息融合的地铁牵引电机轴承故障智能检测方法。首先,选取NU216轴承为研究对象,预制故障缺陷;然后,采用正交试验法设计试验方案,采集NU216轴承的振动信号和声发射信号;其次,将原始数据通过连续小波变换,分别提取轴承的振动和声发射信号的时频域特征,并将2类单通道数据进行融合,得到双通道融合数据集;最后,将得到的3类数据集分别划分为训练集和测试集,输入优化后的卷积神经网络模型进行训练、测试。试验结果表明,基于振动信号的故障诊断准确率为95.76%,基于声发射信号的故障诊断准确率为92.33%,基于融合信号的故障诊断准确率为98.59%。Aiming at the low recognition rate of bearing fault under single sensor,an intelligent detection method for bearing fault of metro traction motor based on optimized CNN and information fusion is proposed.Firstly,NU216 bearing is selected as the research object to prefabricate fault defects;Then,the orthogonal test method is used to design the test scheme,and the vibration signal and acoustic emission signal of NU216 bearing are collected;Secondly,the time and frequency domain characteristics of bearing vibration and acoustic emission signals are extracted from the original data through continuous wavelet transform,and the two types of single channel data are fused to obtain dual channel fusion data sets;Finally,the three kinds of data sets are divided into training set and test set,and the optimized convolutional neural network model is input for training and testing.The test results show that the accuracy of fault diagnosis based on vibration signal is 95.76%,the accuracy of fault diagnosis based on acoustic emission signal is 92.33%,and the accuracy of fault diagnosis based on fusion signal is 98.59%.

关 键 词：参数优化 卷积神经网络 信息融合 地铁牵引电机轴承 故障诊断 

分 类 号：TH133.33[机械工程—机械制造及自动化] U269.6[机械工程—车辆工程]