# Fault Diagnosis Method for Rolling Bearings Based on Two-Channel CNN under Unbalanced Datasets  
Yufeng Qin  
College of Coastal Defense Force, Naval Aviation University, Yantai 264001, China  
Y. Qin  
College of Coastal Defense Force, Naval Aviation University, Yantai 264001, China  
X. Qin  
College of Coastal Defense Force, Naval Aviation University, Yantai 264001, China  
Y. Qin  
different domains are combined. This method is better than the PCA+neural network (NN) and LDA+NN. Compared with AE and DBN, CNN are more advantageous in processing time series data and images [18]. In recent years, researchers have used CNN to extract discriminative features directly. [PERSON] combined the feature extraction and classification stages of CNN to reduce the computational complexity [19]. [PERSON] et al. combined CNN with the second-generation wavelet transform to enhance the robustness of fault diagnosis [20]. [PERSON] et al. enhanced the sensitivity of CNN to fault features through an adaptive weight vector [21]. [PERSON] et al. converted the vibration signal of the rolling bearing into a grayscale image, used the grayscale image to extract fault features, and achieved a better fault diagnosis result [22]. Although all of the above CNN models achieve good diagnostic results, these methods are premised on balanced datasets. However, in the actual working condition, rolling bearings have faults occur infrequently, resulting in insufficient failure data compared to normal data, thus affecting the fault diagnosis accuracy and stability [23]. The imbalanced data make the features learned by CNN more biased to normal state sample features and under-fit to fault sample. The common idea is to compose a balanced dataset by increasing the number of faulty samples, thus eliminating the adverse effects of imbalance learning. [PERSON] et al. proposed the synthetic minority oversampling technique (SMOTE) to randomly generate virtual samples to balance the training set [24]. [PERSON] et al. used a Wasserstein generative adversarial network (WGAN) to balance training samples and reduce differences in fault data distribution [25]. [PERSON] et al. used GAN to generate new samples and diagnosed faults by superposition denoising auto-encoder (SDAE) [26]. [PERSON] et al. applied the fast Fourier transform (FFT) to preprocess the acquired data and generate synthetic minority class samples using GAN [27]. [PERSON] et al. proposed a multi-view GAN (MV-GAN) to extend the image dataset automatically [28]. [PERSON] et al. used GAN to generate empirical mode decomposition (EMD) energy spectrum data, and the fault diagnosis results were better than traditional oversampling techniques [29]. [PERSON] et al. combined adversarial learning with CNN to improve the robustness [30]. [PERSON] et al. used multiple time-frequency feature extraction methods to extract two-dimensional time spectrum images from bearing fault signals and then used single image GAN (SinGAN) to generate additional datasets [31]. [PERSON] et al. used auxiliary classifier GAN with spectral normalization (ACGAN-SN) for bearing fault detection. The experimental results proved that ACGAN-SN has better stability than GAN [32]. [PERSON] et al. modified the GAN generator based on the fault diagnosis results of CNN. In addition, the envelope spectrum error is taken as another correction term, so that fault samples can contain more information [33]. Although all the above methods can solve the fault diagnosis problem under unbalanced datasets to some extent, the following problems exist: generating new samples will change the distribution of the original data, which tends to increase the training time as well as lose important sample information, meaningless noise data may be generated when the data are extremely unbalanced, and the reliability of fault diagnosis results based on noise data are poor. Furthermore, the availability of generated data is related to the adequacy of the initial data. Due to the infrequent occurrence of faults, limited fault data are obtained.