## 2 Materials and Methods
### Methods
#### 2.2.1 Overview
Combined with optimized feature extraction strategies, small-sample learning networks not only improve the generalization ability of models but also enable more efficient and universal solutions for the fault diagnosis and stability assessment of industrial equipment. Future research will focus on further improving and applying small-sample learning methods to enhance model performance under data-scar conditions. For instance, [PERSON] et al. [53] proposed a novel method that achieved remarkable results on small datasets with limited operational conditions, demonstrating the potential of small-sample learning in industrial fault diagnosis. Incorporating such methods is \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{3}{c}{**Performance Corresponding to Different Percentages of Training Data (ACC and F1)**} \\ \hline \multirow{2}{*}{**Extraction**} & \multirow{2}{*}{**Class**} & \multicolumn{2}{c}{**56.5\%**} & \multicolumn{2}{c}{**68.2\%**} & \multicolumn{2}{c}{**88.7\%**} \\ \cline{3-8}  & & **Accuracy** & **F1 Score** & **Accuracy** & **F1 Score** & **Accuracy** & **F1 Score** \\ \hline \multirow{2}{*}{4 s} & 14 & 0.828 & 0.831 & 0.862 & 0.862 & 0.953 & 0.961 \\  & 23 & 0.802 & 0.812 & 0.843 & 0.854 & 0.923 & 0.928 \\ \hline \multirow{2}{*}{10 s} & 14 & 0.839 & 0.846 & 0.897 & 0.890 & 0.964 & 0.967 \\  & 23 & 0.813 & 0.828 & 0.883 & 0.886 & 0.950 & 0.951 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance corresponding to different percentages of training data. expected to further enhance the robustness and practicality of models, providing more reliable technical support for industrial equipment health management and driving the development of related fields. ## 5 Conclusions

This paper proposed a new industrial fault diagnosis method based on the ViT model and advanced data preprocessing techniques. A lightweight front-end encoder, the DWFE, was designed, which enhanced the fine-grained representation of 2D image features through multi-scale depthwise separable convolutions. Subsequently, a self-attention optimization scheme incorporating 2D-RoPE and the ReZero mechanism was proposed, significantly improving the model's ability to capture relative positions and temporal dependencies, while also accelerating training convergence. This study systematically explored the impact of different time-period extraction strategies (sampling intervals and segment lengths) on feature redundancy and training efficiency, providing quantitative evidence for sampling design in engineering practice. The method achieved seamless adaptation across multiple datasets, such as the MCC5 and HUST gearbox datasets, and validated its generalization performance and practical value in complex industrial scenarios. Conceptualization, X.L. and M.W. ; methodology, X.L. and M.W. ; software, X.L. and Z.Z. ; formal analysis, X.L. and Z.Z. ; investigation, X.L. and M.W. ; resources, M.W. ; data curation, X.L. ; writing--original draft preparation, X.L. ; writing--review and editing, X.L. and M.W. ; visualization, X.L. and Z.Z. ; supervision, M.W. ; project administration, M.W. ; funding acquisition, M.W. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Key Research and Development Program of China (Project No. 2022 YFC2407600, Project No. 2022 YFC3601000). Data Availability Statement: Data were derived from public domain resources.