## 1 Introduction
However, GAF is sensitive to noise during the calculation process, which may introduce errors or affect the accuracy of the results ([PERSON] et al., 2019). Using only one method to represent the original vibration signal may lose some signal features and may affect the model's resistance to noise, reducing the robustness of the fault diagnosis method. Therefore, using multiple methods to represent the original signal simultaneously is a better approach. Fault diagnosis models based on LSTM and CNN often have complex structures, limited parallel computing capabilities, and difficulties in learning more global and richer feature representations during the training process. In 2017, [PERSON] et al. (2017) introduced the Transformer, which utilized self-attention mechanisms to quickly capture global sensitive features of time series signals. The emergence of the Transformer sparked a revolution in the field of natural language processing. The attention mechanism can effectively address the challenge of capturing long-range dependencies in time series data and handling of the sequential nature of time series. Indeed, the Transformer architecture discards all recurrent and convolutional structures, enabling parallel computation of sequences and greatly improving training efficiency. The Vision Transformer (ViT) is the application of the Transformer model in the field of image processing. It inherits the multi-head attention mechanism of the Transformer, allowing it to capture global information from images, while abandoning the CNN-based network structures commonly used in computer vision. However, the ViT model has a high demand for training data volume ([PERSON] et al., 2019), and in real-world scenarios, it can be challenging to acquire a sufficient amount of bearing fault data. Transfer learning is commonly used to address such issues ([PERSON] et al., 2019; [PERSON] et al., 2019). It leverages knowledge learned from a source domain dataset to generalize and interpret different but similar or related tasks on the target domain dataset, thereby reducing the requirement for a large number of samples in the target domain dataset ([PERSON] et al., 2019). In recent years, some scholars have introduced transfer learning into the field of bearing fault diagnosis, demonstrating its effectiveness ([PERSON] et al., 2019; [PERSON] et al., 2019; [PERSON] et al., 2019). Based on the analysis above, this paper proposed a new fault diagnosis method for improving the accuracy of bearing fault diagnosis under conditions of small samples, varying loads, and noisy operating conditions. This method incorporated image information fusion and transfer learning with ViT. The main innovations and contributions of this paper are as follows. In response to the problem that a single image generation method is unable to describe the features of time-domain vibration signals from multiple perspectives, resulting in poor generalization and noise resistance of the fault diagnosis method, this paper combined images generated using different methods into Information Fusion Images (IFI) based on image channel splicing. The IFI combined the information provided by the GAF method and the CWT method, which has both time sensitivity and frequency domain information, and can represent the effective information contained in the original time series data more comprehensively, which is conducive to the deep learning model to learn various fault characteristics and improve the accuracy and robustness of fault diagnosis. 2. In order to address challenges such as difficult data collection, limited sample size, and complex operating conditions in real production environments, this paper used a fine-tuned transfer learning Vision Transformer (TLViT) model. The model was pre-trained on a data-rich domain and then transferred to a domain with limited samples. This approach tackles the issue of ViT models requiring a large amount of training data to learn image features and reduces the demand for bearing fault data volume. 3.