[14] proposed a bearing fault diagnosis method based on a one-dimensional convolutional generative adversarial network (1D-DCGAN) and one-dimensional convolutional self encoder (1D-CAE), which realized high-precision diagnosis across different devices with small samples. [PERSON] et al. [15] proposed a rolling bearing fault diagnosis method based on reparametrized VGG (RepVGG), and the recognition and classification accuracy of vibration signals reaches more than 95%. [PERSON] [16] and [PERSON] et al. [17] used the CWRU bearing database as the basic data and used wavelet packet transform and other data processing methods to introduce the processed signals into the improved ResNet model. The signal after processing is imported into the modified ResNet model by using wavelet packet transform and other data processing methods for computational analysis, which effectively solves the overfitting problem and achieves good results at the same time. In addition, [PERSON] et al. [18] proposed a convolutional adaptive model integrating a feature extraction module, a domain adaptive module, and a fault identification module, and used ResNet network as a feature extractor, which can extract the required features from the original signal. In addition, Deep Residual Neural Network (DRSN) is a network architecture proposed in 2020 based on ResNet plus a soft threshold function. [PERSON] et al. (2019) used the DRSN network architecture to achieve intelligent identification and filtering of inertial force interference signals and instrumentation noise signals to improve the accuracy of aerodynamic testing. [PERSON] et al. (2020) proposed a new fault diagnosis method for pressure relief valves, combining the elastic weight consolidation (EWC) algorithm with the Deep Residual Shrinkage Network (DRSN), and the experimental validation of the model yielded an average accuracy rate of 98.8%, with an average loss of 0.095. In summary, the accuracy of fault identification in the literature regarding convolutional neural networks in the field of fault diagnosis is summarized as Table 1.  
[PERSON] et al. (2019) proposed a rolling bearing fault diagnosis method based on variational modal decomposition (VMD), continuous wavelet transform (CWT), convolutional neural network (CNN), and support vector machine (SVM), and verified the effectiveness of the proposed method using the bearing vibration data and spindle unit failure test bench data from Case Western Reserve University (CWRU), and the average classification accuracy of the former was 99.9% and the average classification accuracy of the latter was 90.15%. [PERSON] et al. (2020) proposed a novel attention-guided joint learning convolutional neural network (JL-CNN) for mechanical equipment condition monitoring. The fault diagnosis task (FD task) and signal denoising task (SD task) are integrated into an end-to-end CNN architecture, and good noise robustness is achieved through dual-task joint learning. In addition, CNNs are widely used in fault diagnosis of various mechanical devices ([PERSON] et al., 2019; [PERSON] et al., 2020). [PERSON], [PERSON], and [PERSON] et al. (2020); [PERSON] et al. (2020) extracted data from vibration signals generated when various machines such as bearings are damaged, and detected and processed the signals of various faults by using end-to-end CNN model based on GNR (GTFE-Net), MS-CNN, and other methods, and the final results showed that the optimized convolutional neural network model can be widely used in the field of fault diagnosis of mechanical equipment. In the computational process of neural networks, the choice of hyperparameter values such as learning rate, batch size, hidden layer size, etc. will directly affect the performance and training speed of neural networks. Bayesian optimization is a method to find the optimal hyperparameter settings by modeling the relationship between known hyperparameter configurations and model performance.