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 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.