It uses Bayesian inference and real-time feedback mechanisms to gradually improve the selection of hyperparameters, thus improving the performance of the model. [PERSON] et al. (2020) proposed a wind turbine bearing fault diagnosis strategy based on Bayesian optimization and improved convolutional neural network (CNN) for the problems of weak rolling bearing fault features, difficult extraction and low diagnostic efficiency of wind turbine. The results show that the accuracy of the optimized fault diagnosis model rises by 12.85% compared with the original model. [PERSON] et al. (2020) designed a Bayesian optimization improved LeNet-5 algorithm and carried out experiments through the bearing database, which showed that the bearing fault diagnosis model constructed by this algorithm had an accuracy of 99.94% in the training set, 99.89% in the validation set, and the accuracy of the test set also reached \begin{table}
\begin{tabular}{c c c} \hline \hline
**Literature** & **Diagnostic Methods** & **Accuracy** \\ \hline Literature (2019) & MResNet-SVM & 97.40\% \\ Literature (2019) & MWKCNN & 98.36\% \\ Literature (2019) & FB-LSTM & 98.60\% \\ Literature (2019) & RepVGG & 98.02\% \\ Literature (2019) & GMA-DRSN & 91.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of fault diagnosis accuracy of different models. 99.65%. [PERSON] et al. [30] used Bayesian optimization of hyperparameters such as the initial learning rate, the strength of the L2 regularization, and the stochastic gradient descent momentum in a GoogleNet network to improve the computational accuracy of the original GoogleNet network by 7.35%. [PERSON] et al. [31] optimized a CNN neural network using Bayesian optimization algorithm and compared the computational results with LeNet5, which was 2.92% more accurate than LeNet5. [PERSON] et al. [32] proposed a fault pulse extraction and feature enhancement method for bearing fault diagnosis. The method is able to extract weak fault pulses from bearing signals containing strong background noise, periodic harmonics and strong random pulses. The anti-interference ability of the model is improved. [PERSON] et al. [33] proposed a new method for fault diagnosis of motor bearings based on Extreme Gradient Growth Tree (XGboost) and Bayesian optimization to achieve more accurate identification of vibration signals in the system. In the operational setting of hydrogen refueling stations, bearings frequently become contaminated with acoustic noise, including mechanical vibration. Therefore, it is necessary to employ denoising techniques to mitigate the impact of noise on the acquired signals [34]. When comparing current denoising techniques, it is evident that wavelet denoising and wavelet packet thresholding represent optimal methods for removing mid- and high-frequency noise from large samples with varying degrees of denoising. This conclusion was reached after comparing various denoising techniques, including empirical modal decomposition and variational modal decomposition [34; 35; 36; 37; 38]. [PERSON] et al. [39] compared the performance of wavelet-decomposition-based denoising methods and wavelet-filtering-based denoising methods based on mechanical defect signals. The comparison results show that wavelet filtering is more suitable and reliable for detecting the weak features of mechanical impulse-like defect signals, while wavelet decomposition denoising method is more effective in smoothing signal detection. [PERSON] et al. [40] compared signal analysis techniques for rolling bearing diagnosis and proposed a diagnostic method based on the combination of cepstrum prewhitening and squared-envelope spectroscopy, which was combined with experimental analysis. Finally, it was suggested that the best way to perform condition monitoring should be a combination of classical signal-based and new data-driven techniques. [PERSON] et al. [41] used Debauchies Wavelet-02 (DB-02) to diagnose faults in journal bearings and classified the faults using artificial neural networks (ANN).