## 2 Materials and Methods
### Methods
#### 2.2.1 Overview
When the window length was extended to 10 s extraction, the classification accuracy and precision improved to 0.964 and 0.976, representing an increase of approximately 1.15% in classification accuracy and 1.77% in precision compared to the 4 s extraction window. In the 23-class classification task, the classification accuracy and precision for the 4 s extraction window were 0.923 and 0.932, respectively, while extending the window length to 10 s extraction improved these metrics to 0.950 and 0.949, corresponding to an increase of approximately 2.93% in classification accuracy and 1.82% in precision. Moreover, the 10 s extraction window significantly optimized training efficiency. In the 14-class task, the training time for the 10 s extraction window was 33 s, reducing by approximately 35.39% compared to the 4 s extraction window. In the 23-class task, the training time for the 10 s extraction window was 95 s, representing a reduction of about 22.13% compared to the 4 s extraction window. These results indicate that extending the feature extraction window appropriately not only captures the critical features of time-series data more comprehensively and reduces redundant information but also significantly shortens training time, thereby enhancingboth model performance and computational efficiency. This finding provides an important basis for optimizing feature extraction strategies in multi-class classification tasks. It highlights that selecting an appropriate time window length during model design is crucial for balancing classification performance and computational efficiency. By considering the complexity of classification tasks and computational resource constraints, the model's applicability and robustness in practical industrial scenarios can be further improved. The experiment further explored bearing fault diagnosis performance under different proportions of training data. The experimental results are detailed in Table 10. Through experiments with varying training data proportions, we analyzed the trends in model performance. Additionally, we revealed the model's dependence on data volume, providing a basis for selecting models in specific scenarios and balancing training costs with performance improvements. The experimental results in Table 10 show that as the proportion of training data decreased from 88.7% to 56.5%, the model performance declined significantly. In the 14-class classification task, the classification accuracy of the 4 s extraction window dropped from 0.953 to 0.828, a decrease of approximately 13.13%, while the 10 s extraction window dropped from 0.964 to 0.839, a decrease of about 12.94%. In the 23-class classification task, the classification accuracy of the 4 s extraction window decreased by approximately 13.13%, and the 10 s extraction window decreased by about 14.53%. Overall, as the amount of training data was reduced, the model's performance, including its classification accuracy and F1 score, exhibited a notable decline, particularly in tasks with higher complexity. This result highlights the critical impact of training data quantity on model performance. Notably, the 10 s extraction window demonstrated greater robustness under small-sample conditions. For example, at a training data proportion of 56.5%, the classification accuracy and F1 score of the 10 s extraction window improved by approximately 1.33% and 1.82%, respectively, compared to the 4 s extraction window. These findings indicate that a longer time window can capture key features of time-series data more comprehensively and mitigate the performance degradation caused by insufficient training data. This emphasizes the research value and application potential of small-sample learning methods in industrial scenarios. In practical industrial applications, data collection often faces high costs and limited conditions, resulting in insufficient training data. Small-sample learning methods reduce reliance on large amounts of labeled data, ensuring the stability and accuracy of the model under data-scar conditions.