基于二维图像和CNN-BiGRU网络的滚动轴承故障模式识别

张训杰1,张敏1,2,李贤均1

振动与冲击 ›› 2021, Vol. 40 ›› Issue (23) : 194-201.

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振动与冲击 ›› 2021, Vol. 40 ›› Issue (23) : 194-201.
论文

基于二维图像和CNN-BiGRU网络的滚动轴承故障模式识别

  • 张训杰1,张敏1,2,李贤均1
作者信息 +

Rolling bearing fault mode recognition based on 2D image and CNN-BiGRU

  • ZHANG Xunjie1, ZHANG Min1,2, LI Xianjun1
Author information +
文章历史 +

摘要

为确保对滚动轴承故障诊断的有效性,结合卷积神经网络(CNN)在图像特征提取与分类识别的优势,利用格拉姆角场(GAF)将滚动轴承一维振动信号转换为二维图像数据,既保留了信号完整的信息,也保持着信号对于时间的依赖性。并由此提出基于卷积神经网络与双向门控循环单元(GRU)的诊断模型。首先将二维图像作为模型的输入数据,通过卷积神经网络提取图像的空间特征,再由双向门控循环单元筛选其时间特征,最终由分类器完成模式识别。通过对滚动轴承不同故障程度以及不同故障位置的诊断实验,准确率分别达到99.63%以及99.28%,其效果均优于其他常用算法,证明了所提方法的可行性。

Abstract

In order to ensure the effectiveness of fault diagnosis of rolling bearings, combining the advantages of convolutional neural network in image feature extraction and classification recognition, the one-dimensional vibration signals of rolling bearings were converted into two-dimensional image data by using the gramian angular fields(GAF), which not only retained the complete information of the signal, but also maintained the signal's time dependence. And on this basis, a diagnosis model based on the convolutional neural network and bidirectional gated recurrent unit(GRU) was proposed. First, the two-dimensional image was taken as the input data of the model, and the spatial features of the image were extracted by the convolutional neural network. Then the time features were filtered by the bidirectional gating loop unit, and finally the pattern recognition was completed by the classifier. Through the diagnosis experiments of different fault degree and different fault position of rolling bearings, the accuracy is 99.63% and 99.28% respectively, and their effects are better than other commonly used algorithms, which proves the feasibility of the proposed method.

关键词

滚动轴承 / 故障诊断 / 格拉姆角场(GAF) / 二维图像 / 卷积神经网络 / 双向门控循环单元(GRU)

Key words

rolling bearing / fault diagnosis / gramian angular fields(GAF) / two-dimensional image / convolutional neural network / bidirectional gated recurrent unit(GRU)

引用本文

导出引用
张训杰1,张敏1,2,李贤均1. 基于二维图像和CNN-BiGRU网络的滚动轴承故障模式识别[J]. 振动与冲击, 2021, 40(23): 194-201
ZHANG Xunjie1, ZHANG Min1,2, LI Xianjun1. Rolling bearing fault mode recognition based on 2D image and CNN-BiGRU[J]. Journal of Vibration and Shock, 2021, 40(23): 194-201

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