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 one-dimensional 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.
mining rolling bearingsfault diagnosisConvNeXtSuperlet Transformfull-dimensional dynamic convolutionefficient local attention mechanism
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