目的 轴承作为印刷设备中的旋转核心元件，其运行状态对印刷设备的健康监测作用较大。通过融合小波时频处理与Inception v3模型的优势，提出一种用于印刷设备轴承故障智能诊断方法。方法 利用Morlet小波对采集到的印刷设备轴承原始振动信号进行处理，得到对应的二维时频图像，从时域和频域两方面对轴承故障进行表征;将时频图像作为Inception v3模型的输入，利用其模型的稀疏特性，快速从时频图像中自动学习故障特征，并对其模型参数进行调整;最后，利用训练好的模型实现印刷设备轴承故障诊断。结果 利用印刷设备轴承实验平台对提出方法的有效性进行了验证，实验结果表明该方法的平均诊断精度可达92.53%。结论 与传统智能诊断方法相比，所提方法在诊断精度与稳定性方面均具有一定的优势，可实现高精度印刷设备轴承故障诊断。

As a core rotating component in printing press, the operation status of bearing plays a major role in the health monitoring of printing press. The work aims to propose an intelligent diagnosis method of bearing faults in printing press by mixing the advantages of wavelet time-frequency processing with the Inception v3 model. The Morlet wavelet was used to process the raw vibration signals collected from bearing, and the corresponding two-dimensional time-frequency images were obtained to characterize the bearings faults from the time-domain and frequency-domain. The time-frequency images were used as input of the Inception v3 model, and the filter-level sparsity of the Inception v3 model was used to quickly and automatically learn the fault features from the time-frequency images and adjust the model parameters; finally, the trained model was used to implement the fault diagnosis of printing press bearing. The effectiveness of the proposed method was verified with a printing press experimental platform, and the results indicated that the average diagnostic accuracy of the method can reach 92.53%. Compared with traditional intelligent diagnosis methods, the proposed method has higher diagnosis accuracy and stability to achieve the bearings fault diagnosis of high-precision printing press.