The classification result was 85.7%. In addition, the advantages of using DB-02 wavelet technique over conventional wavelet technique in high-speed machines were experimentally analyzed. The Fourier transform is a method of transforming a signal from a time-domain signal to a frequency-domain signal, and the Fast Fourier Transform (FFT) is an algorithm for efficiently computing the discrete Fourier transform. FFT processing of signals can achieve higher signal accuracy. [PERSON] [42] developed a wireless three-axis rotor sensing system (ORS) and used FFT and Hilbert envelope analysis to greatly improve the accuracy of their system troubleshooting. [PERSON] [43], experimentally and by using the FFT method, investigated the effect of deceleration time on the root mean square (RMS) value of residual vibration (RV). The effect of the result's agreement shows that the FFT method is very effective for studying transient vibration problems in complex systems. [PERSON] et al. [44] proposed a combined model based on RSM-XGBoost and KF algorithm to study the remaining life prediction problem of an aircraft engine, and solved the noise influence problem by adding a Carr filter to achieve higher life prediction accuracy. [PERSON] et al. [45] worked on analyzing the elite RSH by estimating the desired approximation error. Based on the distribution of non-zero elements in the Markov chain transfer matrix, the search process of the elite RSH was classified into three categories, and a general framework for estimating the approximation error, called error analysis, was proposed. To process the CWRU bearing database used in this study, vibration signal window shifting and wavelet denoising were applied. The signals underwent diagnosis and classification using ResNet18. Some hyperparameters were optimized with the Bayesian optimization algorithm to improve fault diagnosis accuracy. Finally, noise was added to replicate real hydrogenation station conditions, ensuring model anti-interference capabilities against noise signals. This paper analyzes and processes the bearing failure signals of Western Reserve University (CWRU). The bearing vibration signal is transformed from a one-dimensional signal to a two-dimensional signal using the Fast Fourier Transform (FFT). The signal samples are split to expand the number of samples by using the vibration signal window spanning. Due to the presence of various noise sources, such as mechanical noise and electromagnetic interference, the vibration signal may be affected during the motor bearing signal acquisition process. Therefore, it is necessary to denoise the decomposed bearing signal. In this paper, we denoise the CWRU signal using a 5-level Symlet-5 wavelet and suppress or eliminate the noise signal using a soft threshold function. The signal is then inputted into various convolutional neural network models for computation. After analyzing the computing time and computational accuracy, this paper selects the resnet18 network to construct the training model. Next, the parameters of the trained neural network model are tuned using Bayesian optimization, based on the theory of neural network hyperparameter optimization, to achieve better fault diagnosis results. To simulate the hydrogen refueling station's production environment and verify the model's anti-interference ability, noise signals of 70 dB, 80 dB, 90 dB, and random pulse signals were added to the CWRU bearing signal. The final test results demonstrate that the BOA-ResNet18 model maintains high fault diagnosis accuracy even with the added noise signals, indicating excellent anti-interference ability.