Fault Diagnosis Method for Rolling Bearing under Variable Working Conditions Using Improved Residual Neural Network

doi:10.7652/xjtuxb202009002

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Fault Diagnosis Method for Rolling Bearing under Variable Working Conditions Using Improved Residual Neural Network

更新时间：2025-07-09

- Fault Diagnosis Method for Rolling Bearing under Variable Working Conditions Using Improved Residual Neural Network
- Vol. 54, Issue 9, Pages: 23-31(2020)
- 作者机构：
  
  1. 兰州理工大学电气工程与信息工程学院,兰州,730050
  2. 甘肃省工业过程先进控制重点实验室,兰州,730050
  3. 兰州理工大学国家级电气与控制工程实验教学中心,兰州,730050
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202009002
  CLC： TP206.3;TH133.3
- Online First：10 September 2020，
  
  Published：2020
- 稿件说明：
移动端阅览
Fault Diagnosis Method for Rolling Bearing under Variable Working Conditions Using Improved Residual Neural Network[J]. 2020, 54(9): 23-31.
DOI：

Fault Diagnosis Method for Rolling Bearing under Variable Working Conditions Using Improved Residual Neural Network[J]. 2020, 54(9): 23-31. DOI： 10.7652/xjtuxb202009002.

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

关键词

Keywords

references

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