For most existing fault diagnosis methods, feature extraction is always based on a complex artificial design and the complete feature extraction from an original signal. With the gradual complication of modern industrial machinery and equipment, it has become more difficult for traditional feature extractors to achieve the desired results. Deep convolutional neural networks (DCNNs) have been developed as effective techniques for fault classification but require large-scale high-intensity computing and prohibitive hardware resource requirements. This paper proposes a lightweight CNN that can be easily used for the fault diagnosis of rotating machinery by adjusting the network structure and optimizing the network. First, the raw vibration acceleration signal is transformed into a two-dimensional gray image. Second, two mature and commonly used modules named LeNet and NIN are combined to form a new model with a simple structure. Then, through parameter adjustment and optimization, an improved and optimized CNN with a lightweight structure and fewer parameters is constructed. The experimental verification has shown that this method has high accuracy and stability in fault diagnosis. Finally, the application of this new network for the fault diagnosis of rolling bearings with different damage levels but similar fault types shows high diagnostic accuracy and good generalization ability. In addition, we attempt to explain how the feature filters of a CNN work by visualizing the convolutional layer of the network.

对于大多数现有的故障诊断方法，特征提取始终基于复杂的人工设计，并基于原始信号进行完整的特征提取。随着现代工业机械和设备的逐渐复杂化，传统特征提取器变得越来越难以获得期望的结果。深度卷积神经网络（DCNN）已被开发为用于故障分类的有效技术，但需要大规模的高强度计算和难以满足的硬件资源要求。本文提出了一种轻量级的CNN，通过调整网络结构和优化网络，可以轻松地将其用于旋转机械的故障诊断。首先，原始振动加速度信号被转换为二维灰度图像。第二，两个名为LeNet和NIN的成熟且常用的模块结合在一起，形成了一个具有简单结构的新模型。然后，通过参数调整和优化，构建了结构轻巧，参数较少的改进和优化的CNN。实验证明，该方法在故障诊断中具有较高的准确性和稳定性。最后，该新网络在故障等级不同但故障类型相似的滚动轴承故障诊断中的应用显示出较高的诊断精度和良好的泛化能力。此外，我们试图通过可视化网络的卷积层来说明CNN的特征过滤器如何工作。构建了一种结构轻巧，参数较少的改进和优化的CNN。实验证明，该方法在故障诊断中具有较高的准确性和稳定性。最后，该新网络在故障等级不同但故障类型相似的滚动轴承故障诊断中的应用显示出较高的诊断精度和良好的泛化能力。此外，我们试图通过可视化网络的卷积层来说明CNN的特征过滤器如何工作。构建了一种结构轻巧，参数较少的改进和优化的CNN。实验证明，该方法在故障诊断中具有较高的准确性和稳定性。最后，该新网络在故障等级不同但故障类型相似的滚动轴承故障诊断中的应用显示出较高的诊断精度和良好的泛化能力。此外，我们试图通过可视化网络的卷积层来说明CNN的特征过滤器如何工作。该新网络在损伤程度不同但故障类型相似的滚动轴承故障诊断中的应用显示出较高的诊断精度和良好的泛化能力。此外，我们试图通过可视化网络的卷积层来说明CNN的特征过滤器如何工作。该新网络在损伤程度不同但故障类型相似的滚动轴承故障诊断中的应用显示出较高的诊断精度和良好的泛化能力。此外，我们试图通过可视化网络的卷积层来说明CNN的特征过滤器如何工作。