目的 为解决轴承故障特征时频图像难以识别的问题，在进行时频图像训练和学习故障特征的基础上，提出新的故障诊断方法。方法 本文提出一种MDCNet网络，该网络由多尺寸卷积核模块(Multi-Size Convolution Kernel Module)、双通道池化层(Dual-Channel Pooling Layer)和跨阶段部分网络(Cross Stage Partial Network)组成。首先，将采集的振动信号经过同步压缩变换，得到信号的瞬时频率图像，然后输入神经网络获得故障诊断结果。结果 将提出的方法在西储大学轴承数据集进行预测，准确率达到了99.9%。与AlexNet、VGG–16、Resnet等传统方法进行对比试验，结果表明MDCNet方法分类精度可达99.9%，高于传统方法的分类精度(95.70%、98.51%、97.64%)。结论 结果表明，本文所提出方法的预测准确率高于其他方法的，验证了该方法在包装机械故障诊断中是可行的。

The work aims to propose a new fault diagnosis method based on time-frequency image training and fault feature learning, in order to solve the problem that the time-frequency image of bearing fault feature is difficult to recognize. MDCNet network was proposed, which was composed of Multi-Size Convolution Kernel Module, Dual-Channel Pooling Layer and Cross Stage Partial Network. Firstly, the acquired vibration signal was compressed and transformed synchronously to obtain the instantaneous frequency image of the signal. Finally, the fault diagnosis result was obtained by inputting the neural network. The prediction accuracy of the proposed method was 99.9% after applied to the bearing data set of Case Western Reserve University. Compared with AlexNet, VGG -- 16, Resnet and other traditional methods, MDCNet method realized a classification accuracy of 99.9%, which was higher than the classification accuracy of 95.70%, 98.51% and 97.64% of traditional methods. The results show that the prediction accuracy of the proposed method is higher than that of other methods, which verifies the feasibility of the proposed method in fault diagnosis of packaging machinery.