基于超小波变换与OD−ConvNeXt−ELA的矿用滚动轴承故障诊断  

Fault diagnosis of mining rolling bearings based on Superlet Transform and OD-ConvNeXt-ELA

作　　者：吴新忠 罗康 唐守锋 何泽旭 陈琪 WU Xinzhong;LUO Kang;TANG Shoufeng;HE Zexu;CHEN Qi(School of Information and Control Engineering,China University of Mining and Technology,Xuzhou 221116,China)

机构地区：[1]中国矿业大学信息与控制工程学院,江苏徐州221116

出　　处：《工矿自动化》2024年第12期120-127,共8页Journal Of Mine Automation

基　　金：国家重点研发计划项目(2018YFC0808100);江苏省重点研发计划项目(BE2016046)。

摘　　要：针对现有矿用滚动轴承故障诊断方法存在特征提取能力有限、泛化性欠佳的问题,提出了一种基于超小波变换(SLT)与OD−ConvNeXt−ELA的矿用滚动轴承故障诊断方法。以ConvNeXt−T为基础,引入批归一化(BN)技术以提高网络的泛化性,使用全维动态卷积(ODConv)替换原有的深度可分离卷积,以提高网络的适应性,引入高效局部注意力(ELA)以使网络聚焦关键位置特征,构建了矿用滚动轴承故障诊断OD−ConvNeXt−ELA网络模型;为充分利用OD−ConvNeXt−ELA网络模型的图像特征提取能力,选用SLT将采集的滚动轴承一维振动信号转换为二维时频图像后输入OD−ConvNeXt−ELA进行模型训练。选用凯斯西储大学(CWRU)和帕德博恩大学(PU)轴承数据集进行故障诊断实验,结果表明:对于单一工况下的CWRU轴承数据集,OD−ConvNeXt−ELA平均故障诊断准确率为99.65%,较ConvNeXt−T提高了1.61%;对于跨工况下的CWRU轴承数据集,OD−ConvNeXt−ELA平均故障诊断准确率为87.50%,较ConvNeXt−T提高了3.30%;对于跨工况下的PU轴承数据集,OD−ConvNeXt−ELA平均故障诊断准确率为89.33%,较ConvNeXt−T提高了3.46%;基于SLT与OD−ConvNeXt−ELA的矿用滚动轴承故障诊断方法在跨轴承、跨工况及噪声干扰下具有准确率高、泛化能力强的优势。In response to the limitations of current fault diagnosis methods for mining rolling bearings,which suffer from limited feature extraction capabilities and poor generalization,a fault diagnosis method based on Superlet Transform(SLT)and OD-ConvNeXt-ELA was proposed.Built upon ConvNeXt-T,Batch Normalization(BN)technology was introduced to improve the network's generalization ability.Omni-dimensional Dynamic Convolution(ODConv)replaced the original depthwise separable convolution to enhance the adaptability of the network.Efficient Local Attention(ELA)was incorporated to focus the network on key feature locations.This formed the OD-ConvNeXt-ELA network model for fault diagnosis of mining rolling bearings.To fully leverage the image feature extraction ability of the OD-ConvNeXt-ELA model,SLT was used to convert the collected onedimensional vibration signal of the rolling bearing into a two-dimensional time-frequency image,which was then input into the OD-ConvNeXt-ELA for model training.Fault diagnosis experiments were conducted using the bearing datasets from Case Western Reserve University(CWRU)and Paderborn University(PU).The results showed that for the CWRU bearing dataset under a single operating condition,the average fault diagnosis accuracy of OD-ConvNeXt-ELA was 99.65%,which was an improvement of 1.61%over ConvNeXt-T.For the CWRU bearing dataset under cross-operating conditions,the average fault diagnosis accuracy of OD-ConvNeXt-ELA was 87.50%,which was an improvement of 3.30%over ConvNeXt-T.For the PU bearing dataset under cross-operating conditions,the average fault diagnosis accuracy of OD-ConvNeXt-ELA was 89.33%,an improvement of 3.46%over ConvNeXt-T.The fault diagnosis method based on SLT and OD-ConvNeXt-ELA shows high accuracy and strong generalization ability under cross-bearing,cross-operating conditions,and noise interference.

关 键 词：矿用滚动轴承 故障诊断 ConvNeXt 超小波变换 全维动态卷积 高效局部注意力机制 

分 类 号：TD67[矿业工程—矿山机电]