## 1 Introduction
The identification types included gear crack, wear, and plastic deformation. The subsequent results show that the features extracted by MVSF resulted in the highest recognition rate, 98.5% [5]. [PERSON] et al. used ensemble empirical mode decomposition (EEMD) to denoise and reconstruct original vibration signals to extract significant fault features. They built an output-input-hidden feedback (OIIHF) Elman neural network and used the AdaBoost-bagging double ensemble algorithm to optimize it. The method was used to identify the gearbox's missing tooth and crack faults. The results show that the recognition accuracy of the method was 90.6%, higher than the other methods that used AdaBoost or bagging alone or did not use AdaBoost and bagging [17]. [PERSON] et al. proposed an improved Hilbert-Huang transformation (HHT) to extract fault features, and the method analyzed time-domain signals so that the features were more concise. The pairwise-coupled sparse Bayesian extreme learning machine (PC-SBELM) algorithm was used for recognition, and the missing tooth, crack and wear fault types were established. The method was compared with pairwise-coupled probabilistic neural networks (PC-PNN) and pairwise-coupled relevance vector machine (PC-RVM). The results of [PERSON] et al. show that the PC-SBELM had a shorter training time and higher recognition rate than PC-PNN and PC-RVM; the recognition rate was 93.92% [18]. Regarding fault diagnosis, [PERSON] et al. proposed a classifier mixed with a genetic algorithm (GA) and SVM. The method was used to identify missing tooth and crack fault types and compare them with traditional SVM recognition rates. Their results show that the recognition rate of traditional SVM was 91.5%. Further, the recognition accuracy of the SVM optimized by GA was 98.7%. The advantage is that the GA-SVM had the SVM classifier of multi-class radial basis function (RBF) of the one-against-one (OAO) classification strategy, which could optimize the accuracy of classification to the maximum extent [19]. [PERSON] et al. developed an MGGASVM model based on GA optimization and a fuzzy logic (FL) model to identify the cracks, wear, surface fatigue, and plastic deformation of gears. The recognition rate of MGGASVM was 97%, and the recognition rate of the FL-based model was 96% [20]. The reference for gearbox fault models and recognition rates of the methods mentioned above are detailed in Table 1.  
Signals collected by vibration sensors during the operation of a wind turbine gearbox are susceptible to interference from other coupling units, e.g., blades, hubs, or generators. Noise signals generated by nearby components can often mix with the vibration signals of gearboxes so that the actual gearbox fault type cannot be identified. As a new signal analysis method, the SDP can fully describe the characteristics of signals so that subtle differences in input signals are more apparent. These subtle differences represent and allow for the detection of the essential features of any vibration signal, making input signals more favorable for analyses. When the amplitude of a characteristic signal is lower than the background noise, the SDP can extract the signal features effectively [21] and highlight them in a point symmetry graph. Referring to the above literature, most of the studies proposed their own feature extraction and recognition methods for wind turbine gearboxes. However, most of them identified data signals, and few studied image recognition. Furthermore, most of the said methods removed noise numerically before feature extraction. The SDP has good resistance to noise; the noise is eliminated while the data are made into an image, and the fault features can be extracted. CNN matches images and the topological structure of networks well, and the weight-sharing characteristic can reduce the complexity of network models.