## 2 Research Status
### Research Status of Small Sample Issues in Fault Diagnosis
The framework employs a transfer learning strategy combined with the adaptive mixup method to adaptively expand the fault samples to ensure the number and diversity of extended samples and uses the transformation matrix as the evolutionary channel to reduce the diagnostic error with the increase in fault samples without retraining the framework. Based on simulation data, [PERSON] [18] proposed a fault diagnosis method combining convolutional neural networks and parameter transfer strategies, which avoids the problem of diagnosis accuracy caused by insufficient model training under small samples. [PERSON] [19] constructed an engine fault diagnosis framework combining deep auto-encoders with transfer learning. The framework uses a deep auto-encoder to establish an engine fault feature extraction model with sufficient samples and transfer learning to extract features in small samples, using a support vector machine as a classifier to complete fault classification of small samples. [PERSON] [20] used a global average pooling layer instead of the fully connected layer to reduce the number of parameters to be trained in the convolutional neural network. Based on the improved transfer learning method of pre-training and fine tuning, it avoids the problem of overfitting in the case of small samples and the fault diagnosis task in the same scenario. The classification accuracy of the method was 92.25% when fine tuning was performed with 1% of the training set data in the target domain. [PERSON] [21], based on the transfer learning framework, added a large amount of source data with different distributions as training data to the target data and used the convolutional neural network as the base learner to update the weights of the training samples by employing the improved Tr AdaBoost algorithm. This formed a high-performance diagnostic model, improving the diagnostic accuracy in case of insufficient data in the target domain. (3)  
Research on methods based on generative adversarial learning  
Transfer learning has a significant effect on the fault diagnosis of small samples, but it is difficult to find a suitable adaptive source domain for fault diagnosis knowledge transfer in equipment with complex structures (for example, the lack of fault data is common in the box-type substation targeted in this paper). With the emergence of GANs [22], more and more scholars have been focusing on the input source layer to solve the problem of fault diagnosis of small samples. Some scholars [23] expanded the bearing vibration signal of small samples using WGAN with a gradient penalty as the data generation model and used the expanded samples as the input of the self-attention convolutional neural network for fault diagnosis. This effectively improved the accuracy of bearing fault diagnosis under small samples. The scholars in [24] proposed a fault diagnosis method combining a generative adversarial network with transfer learning, which used a generative adversarial network to generate dummy samples with similar fault characteristics to actual engineering monitoring data and then introduced domain adaptation regular term constraints in the residual network training process to form a deep transfer fault diagnosis model. This effectively addressed the problem of low accuracy of the fault diagnosis model caused by insufficient available data of mechanical equipment and large data distribution differences under multiple working conditions in practical applications. [PERSON] [25] introduced a dropout layer into the auxiliary classifier generative adversarial network (AC-GAN) to prevent the model from generating duplicate samples and added a convolutional layer to the AC-GAN discriminant to improve the anti-noise ability of the discriminator. This was performed to enhance the performance of the auxiliary classification generative adversarial network and generate a large number of high-quality fault samples. This approach solves the problem of a low fault recognition rate in the case of small samples.