Traditional methods of rolling bearing fault diagnosis generally have the following disadvantages: low accuracy of fault severity identification, the need for artificial feature extraction, poor noise resistance and high requirements for diagnostic equipment. To overcome these disadvantages, an intelligent bearing fault diagnosis method based on Stacked Inverted Residual Convolution Neural Network (SIRCNN) is proposed. Compared with machine learning and classical convolutional neural networks, SIRCNN has a smaller model size, faster diagnosis speed and extraordinary robustness. The lightweight of the model is achieved through the application of depthwise separable convolution. Moreover, using the inverted residual structure ensures the accuracy of the model in noisy environments. The experimental results show that the fault diagnosis of rolling bearing based on SIRCNN can effectively identify the type and severity of bearing fault under different noise environments, improve the diagnostic efficiency and reduce the performance requirements for the diagnostic equipment.

传统的滚动轴承故障诊断方法通常具有以下缺点：故障严重性识别的准确性低，需要人工提取特征，抗噪声性差以及对诊断设备的要求很高。为了克服这些缺点，提出了一种基于堆叠逆残卷积神经网络（SIRCNN）的智能轴承故障诊断方法。与机器学习和经典卷积神经网络相比，SIRCNN具有更小的模型大小，更快的诊断速度和非凡的鲁棒性。该模型的轻量化是通过应用深度可分离卷积实现的。此外，使用倒置残差结构可确保在嘈杂环境中模型的准确性。