[1]赵小强,梁浩鹏.使用改进残差神经网络的滚动轴承变工况故障诊断方法[J].西安交通大学学报,2020,54(09):023-31.[doi:10.7652/xjtuxb202009002]
 ZHAO Xiaoqiang,LIANG Haopeng.Fault Diagnosis Method for Rolling Bearing under Variable Working Conditions Using Improved Residual Neural Network[J].Journal of Xi'an Jiaotong University,2020,54(09):023-31.[doi:10.7652/xjtuxb202009002]
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使用改进残差神经网络的滚动轴承变工况故障诊断方法
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《西安交通大学学报》[ISSN:0253-987X/CN:61-1069/T]

卷:
54
期数:
2020年第09期
页码:
023-31
栏目:
出版日期:
2020-09-10

文章信息/Info

Title:
Fault Diagnosis Method for Rolling Bearing under Variable Working Conditions Using Improved Residual Neural Network
文章编号:
0253-987X(2020)09-0023-09
作者:
赵小强123 梁浩鹏1
1.兰州理工大学电气工程与信息工程学院, 730050, 兰州; 2.甘肃省工业过程先进控制重点实验室, 730050, 兰州; 3.兰州理工大学国家级电气与控制工程实验教学中心, 730050, 兰州
Author(s):
ZHAO Xiaoqiang123 LIANG Haopeng1
1. College of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China; 2. Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou 730050, China; 3. National Experimental Teaching Center of Electrical and Control Engineering, Lanzhou University of Technology, Lanzhou 730050, China
关键词:
故障诊断 滚动轴承 变工况 残差神经网络
Keywords:
fault diagnosis rolling bearing variable working condition residual neural network
分类号:
TH133.3; TP206.3
DOI:
10.7652/xjtuxb202009002
文献标志码:
A
摘要:
针对滚动轴承工况复杂多变、环境噪声干扰大、有效数据样本不足而导致的故障诊断效果不佳的问题,提出了一种用于滚动轴承变工况故障诊断的改进残差神经网络方法。以采集到的滚动轴承时域信号作为输入,针对滚动轴承时域信号时变性较强的特点,构建了一种基于Inception模块改进的数据池化层。基于Inception模块思想,采用3个3×3的小卷积层串联和堆叠以及加入残差连接的方式构建数据池化层,有效地提取了特征信息。在残差块中添加跳跃连接线,设计了一种带跳跃连接线的残差块,增强了残差块对特征信息的学习效率。利用空洞卷积能够扩大感受野的优点,将带跳跃连接线的残差块中的普通卷积替换为空洞卷积,设计了一种带跳跃连接线的空洞残差块。将设计的两种残差块端对端首尾相连构建神经网络。将所提方法与SVM+EMD+Hilbert包络谱、BPNN+EMD+Hilbert包络谱和ResNet方法进行了仿真对比,结果表明,所提方法在变噪声实验中的平均准确率为97.34%,变负荷实验中的准确率为88.83%~96.76%,均高于其他方法的,变工况实验中的平均准确率高于ResNet方法的,且具有更低的均值方差0.000 6。所提方法具有较强的抗噪性和泛化能力。
Abstract:
Aiming at the bad effect of fault diagnosis of rolling bearing due to complex and changeable working environments, ambient noise influence and insufficient valid sample data, an improved residual neural network method for fault diagnosis is proposed under variable working conditions. The acquired time domain signals of rolling bearing are taken as the inputs, and according to the strong time-varying characteristic of time domain signals of rolling bearing, an improved data pooling layer based on the Inception module is constructed. To extract the feature information effectively, following the Inception module idea, the data pooling layer is constructed by three small 3×3 stacked convolutional layers in series and by adding residual connection. A kind of residual block with a skipping connecting line is designed by adding a skipping connecting line, which can enhance the learning efficiency of characteristic information. Because the dilated convolution can expand the receptive field, the normal convolution in the residual block with a skipping connecting line is replaced by a dilated block, so a dilated and residual block with skipping connecting line is designed. The neural network is designed by the two kinds of residual blocks in end-to-end connection. Compared with SVM+EMD+Hilbert envelope spectrum, BPNN+EMD+Hilbert envelope spectrum and ResNet, the results show that the average accuracy of the proposed method in the variable noise experiment is 97.34%, the accuracy in the variable load experiment is 88.83% - 96.76%, which are higher than the other methods, and the average accuracy is higher than ResNet method with a lower mean variance of 0.000 6 in the variable working condition experiment, so the noise resistance and generalization ability of the proposed method are verified.

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备注/Memo

备注/Memo:
收稿日期: 2020-03-23。作者简介: 赵小强(1969—),男,教授,博士生导师。基金项目: 国家自然科学基金资助项目(61763029); 甘肃省高等学校产业支撑引导项目(2019C-05); 甘肃省工业过程先进控制重点实验室开放基金资助项目(2019 KFJJ01)。
更新日期/Last Update: 2020-09-10