## 4 Conclusions  
A TLViT + IFI fault diagnosis method based on image information fusion was proposed to solve the problem of variable load conditions, less dataset, and noise in the working environment. The original signal was transformed into a grayscale image by using CWT, GASF, and GADF. This fused image effectively represented the signal characteristics in a comprehensive manner. Subsequently, the obtained IFI was input into the ViT for fault diagnosis based on transfer learning training. Consequently, the following conclusions can be derived. In the model performance comparison experiment, the TLViT model trained based on transfer learning outperformed TLResnet50 under varying loads and small samples. When the number of training samples is 100, the fault diagnosis accuracy on the test set is 97.34%, indicating that this method can be applied to the situation with few training  
Figure 9: The diagnosis accuracy of each method under different SNR. samples. The proposed method has an average diagnosis accuracy of 98.96% in the variable load condition, which is higher than other models, indicating that the fault diagnosis model trained by this method has good generalization. The proposed IFI is more effective than the single image coding method. The test results of anti-noise performance show that when the SNR is \(-4\) dB, the TLViT model is difficult to accurately diagnose the faults in the images generated by the control group method. However, the TLViT + IFI method can achieve a diagnosis accuracy of 69.10% on test set. It can be seen that IFI has higher anti-noise ability than the pseudo-color images generated using CWT, GADF, and GASF. The above results show that the proposed TLViT + IFI fault diagnosis method has strong generalization and robustness under variable load, small sample, and noise environment, and can provide an effective idea for the research of rolling bearing fault diagnosis. Theoretically, this method can be used to diagnose the fault information in various time series forms, but only the vibration signal of the bearing, namely the acceleration sensor data, is discussed in this paper. In this paper, only the CWRU dataset was used for training and verification, and the bearing types and working conditions in the dataset were limited. In the process of converting time series data into IFIs, three methods, GASF, GADF, and CWT, need to be used first to generate grayscale maps, which increases the calculation cost. To solve the above problems, the author will carry out the following work in the follow-up research. (1) Explore the effect of combining other types of sensor data (such as temperature and sound signals) with the method proposed in this paper. (2) Validate the proposed method in more realistic and complex work environments. (3) The parameter volume of the Resnet50 model is 25.6 M [41], while the parameter volume of the ViT model used in this paper is 86M [25]; therefore, the lightweight treatment of the ViT model will be carried out in subsequent research, and the goal is to reduce its parameter volume by 50%. Z.Z. : Methodology, Formal analysis, Software, Validation and Writing--original draft. J.L. : Funding acquisition, Project administration, Formal analysis and Validation. C.C. : Funding acquisition, Methodology, Project administration, and Writing--review and editing. J.R.: Methodology, Formal analysis, Investigation and Writing--review and editing, Y.X. : Formal analysis, Investigation, and Writing--original draft. All authors have read and agreed to the published version of the manuscript. This work was supported by the Hebei Nature Science Foundation under grant no. E2024402079, and Key Laboratory of Intelligent Industrial Equipment Technology of Hebei Province (Hebei University of Engineering) under grant no. 202206. Not applicable. The data were collected from a publicly available CWRU bearing database.