Convolutional Neural Networks have promoted development of the fault diagnosis in the machine prognostics and health management. However, the existing methods have limited applicability under strong noisy conditions. We propose an attention-based ConvNeXt with a parallel multiscale dilated convolution residual module for the rotor fault diagnosis. The parallel multiscale dilated convolution residual module is firstly introduced to filter noise and extract multiscale discriminative features intelligently. The multi-head attention module is then embedded in ConvNeXt for global discriminative features adaptively. Focal Loss is finally implemented for identifying hard-to-classify samples to further improve the diagnostic accuracy. The application results demonstrate the superiority and robustness of our method in two case studies.

中文翻译：

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基于注意力的 ConvNeXt 具有并行多尺度扩张卷积残差模块，用于旋转机械故障诊断

卷积神经网络促进了机器预测和健康管理中故障诊断的发展。然而，现有方法在强噪声条件下的适用性有限。我们提出了一种基于注意力的 ConvNeXt，具有并行多尺度扩张卷积残差模块，用于转子故障诊断。首先引入并行多尺度扩张卷积残差模块来过滤噪声并智能地提取多尺度判别特征。然后将多头注意力模块嵌入到 ConvNeXt 中，以自适应地获得全局判别特征。最后采用Focal Loss来识别难以分类的样本，进一步提高诊断准确性。应用结果在两个案例研究中证明了我们的方法的优越性和稳健性。