Abstract: Aiming at the shortcomings of traditional mechanical fault detection methods based on vibration signals that multiple feature quantities need to be selected, this paper introduces a mechanical fault identification method for distribution transformer based on complete ensemble empirical mode decomposition with adaptive noise（CEEMDAN） and Gramian angular field（GAF）. This method uses CEEMDAN to reconstruct the signal and applies GAF transformation to obtain a two-dimensional image of the reconstructed signal. After the two-dimensional image is gray-scaled and binarized, the resulting binary matrix is used to train the radial basis function（RBF） neural network to realize the detection of mechanical faults. A transformer was used to simulate and test the fault, and the results show that the proposed method is accurate and effective. In engineering practice, the classification function of the RBF neural network can be optimized by continuously collecting a large number of transformer operating data, which can realize the accurate identification of different types of faults, and has high reference value.