The operating conditions of planetary transmission are mostly non-stationary operating conditions. During the operation, the gear meshing vibration signals are coupled with each other, which leads to the aliasing of test signals, and the difficulty of hidden fault diagnosis increases. At the same time, when applying complex neural network models for fault diagnosis and prediction, most of them will be limited by the hardware of industrial field edge computing equipment. Aiming at the related problems, an intelligent recognition model based on smooth and pseudo Wigner-Vile distribution (SPWVD) and knowledge distillation is proposed to reduce the parameters of the network model while ensuring the accuracy of planetary transmission fault diagnosis. First, the ensemble empirical mode decomposition (EEMD) method is used to decompose the multi-component vibration signal, then the single-component signal is selected for SPWVD calculation and linearly superimposed to obtain a two-dimensional time-frequency diagram as input. The ResNet101 is used as the teacher model to guide the student model MobileNet for training. The complex teacher model imparts the knowledge in the data to the student model, which improves the accuracy of the student model.The method is compared with similar methods. The results show that the storage cost of the model is reduced to 24.55% of the teacher model at the expense of 2.43% accuracy, which is 9.61% higher than that of MobileNet without knowledge distillation. This research method provides an effective and feasible solution to improve the practical application of deep learning model in engineering and reduce the deployment cost of edge computing equipment.