## 1 Introduction
[13] used the series structure of 1D-CNN and LSTM to realize bearing fault diagnosis, and the diagnosis accuracy was higher than that of the single 1D-CNN model and LSTM model. However, for a too-long time series, LSTM is still prone to gradient disappearance and gradient explosion in the calculation process, and because of the cyclic structure of LSTM model, it is difficult to carry out effective parallel calculation. The above methods are all proposed based on the original one-dimensional vibration signal data. At present, more and more scholars are beginning to convert one-dimensional data into two-dimensional images and make use of the advantages of deep learning models in the field of computer vision to achieve better diagnosis results. [PERSON] et al. [14] converted one-dimensional vibration signals into two-dimensional gray images and sent the images into CNN for bearing fault diagnosis, making full use of the advantages of CNN that are good at extracting image features and achieving better diagnosis results. [PERSON] et al. [15] directly took the bearing vibration waveform as the two-dimensional image input of the CNN model to obtain better real-time performance. [PERSON] et al. [16] proposed a new method to transform one-dimensional data into a two-dimensional image without any predefined parameters, eliminating the influence of expert's knowledge as much as possible. The image was trained in the LeNet-5 model based on CNN, and the proposed method which was tested on three famous datasets, including motor bearing dataset, self-priming centrifugal pump dataset, and axial piston hydraulic pump dataset, and had achieved a prediction accuracy of 99.79%, 99.481%, and 100%, respectively. [PERSON] et al. [17] utilized the Adaptive Optimal-Kernel Time-Frequency Representation (AOK-TFR) algorithm to transform time series into time-frequency spectrogram representations, thereby simultaneously capturing signal characteristics from both time and frequency domains. In recent years, researchers have introduced attention mechanisms into deep learning to address the drawbacks of traditional models, such as high complexity, large number of parameters, and high computational cost. The attention mechanism is derived from human visual attention and can automatically learn to focus on different parts of the input, thus enhancing the performance of the model. Through this approach, the model can better comprehend the intrinsic structure and semantic information of the input data. Introducing attention mechanism into deep learning models for fault diagnosis tasks has also become a research trend. [PERSON] et al. (2018) proposed an attention-based CNN-BiLSTM network, which achieved a diagnosis accuracy of 96.58% even under noisy conditions. [PERSON] et al. (2019) introduced a multi-scale feature fusion CNN with an attention mechanism for bearing fault diagnosis. Common methods used to convert one-dimensional vibration signals into two-dimensional images include continuous wavelet transform (CWT) ([PERSON] et al., 2019), short-time Fourier transform (STFT) ([PERSON] et al., 2019), and Gramian angular field (GAF) ([PERSON] et al., 2019). Among them, CWT and STFT are both time-frequency analysis methods, which transform signals from the time domain to the frequency domain for analysis. CWT enables multi-scale analysis of signals, thereby providing more detailed information in both time and frequency domains, making it suitable for analyzing non-stationary signals. Compared to STFT, CWT has better capability to capture the local time-frequency features of a signal, allowing for a more accurate determination of the signal's local characteristics in both time and frequency domains. However, CWT is based on a continuous scale transformation, which means that the choice of scale can impact the analysis results. GAF is an image coding method for time series data. It can effectively capture the similarity and correlation between time series by computing the inner product between vectors, revealing their dynamic relationships.