## 1 Introduction  
The intelligent fault diagnosis used on induction motors has increased in recent years. Since induction motors play a significant role in the industry to ensure operational security and avoid faults occurrence, fault detection has become one of the research goals. The related technique can divide into statistical models and machine learning methods. Among them, statistical-based models involve procedures such as the-Wiener process [1], particle filter [2], and Kalman filter [3]. The statistical model requires the knowledge of the professional field to manually extract the characteristics of the signal and perform statistical fault classification, resulting in a higher threshold for establishing a fault diagnosis model. Therefore, there are more machine learning methods widely used in the fault diagnosis of induction motors, including convolutional neural networks (CNN) [4], recurrent neural networks (RNN) [5], Artificial Neural Network (ANN) [6], deep autoencoders [7], etc. The above methods have achieved good accuracy in the fault diagnosis of induction motors. Meanwhile, they significantly reduced manually extracting features' labor and time costs. However, the statistical models and machine learning methods above can only obtain high classification accuracy with sufficient and balanced data. Under general conditions, the induction motors are mainly operating in a normal and healthy state, which caused mostly unbalanced data to be captured. Therefore, the performance and practicability of the fault diagnosis model are reduced, so the unbalanced training data become one of the unsolved tasks in diagnostic research. At present, the methods used to solve the problem of data imbalance are mostly under-sampling and over-sampling. Among them, under-sampling reduces the number of most datasets to balance the data, while over-sampling [8] is the opposite. To increase the number of minority datasets to expand tradition, among the methods for generating similar data, the Synthetic Minority Over-Sampling Technique (SMOTE) [9] and Adaptive synthesis were standard in the past. The synthetic Over-Sampling Approach (ADASYN) [10] is to synthesize new data through interpolation between actual data and the data of the nearest adjacent edge. Although this method can obtain newly generated data, if there are too little data, there is a problem of overfitting, which makes the generation effect unsuitable, so the method of deep learning is extended. The most representative solution is Generative Adversarial Network (GAN) [11]. In [12; 13; 14], the author uses GAN to train noise distribution by learning the actual vibration signal, and then generating realistic data to balance and expand the available dataset. Using the experiment proves that this method provides an effective tool for fault diagnosis in the environment of data imbalance and noise. The authors in [15] proposed an unsupervised learning method for bearing fault diagnosis by combining the Short-Time Fourier Transform (STFT) and the Classification Generative Confrontation Network (CatGAN). In the experimental analysis, the effectiveness of the proposed method is verified, including high diagnostic ability and resistance to load changes. The authors in [16] proposed a fault diagnosis method for rotating electric machines using a GAN with multiple sensing signal fusion technologies (Data-Fusion). Through experiments on two rotating machinery datasets and comparison with other data generation techniques, the superiority of this method in fault diagnosis application is verified. Finally, given the current data imbalance research, most of them are aimed at different improved versions of GAN to improve fault diagnosis accuracy. However, there has not been a comprehensive comparison of different signal processing, load conditions, and data enhancement methods in the study of training data imbalance.