# Lab-to-Field Generalization Gap: Assessment of Transfer Learning for Bearing Fault Detection
## 3 Results and Discussions
### General Results  
The classification accuracies for all source-to-target transfer scenarios are shown in Figure 7. For the baseline Scenario Zero, the ML accuracy is shown by the blue horizontal dashed line, and the TL accuracy is shown by the red dashed line. Overall, the TL model achieved significantly higher classification accuracy than the ML model, with improvements exceeding 30% in some cases, such as in the strong CN scenario with 100% data availability. The grey dashed vertical lines indicate the accuracy difference between the ML and TL models within each scenario where TL performed better. There are three exceptional cases in which the ML model performs better than the TL model, marked  
Figure 6: Classification accuracy, sensitivity, and specificity values of ML and TL models for each investigated scenario. by red vertical lines. These correspond to the EN scenarios. In these cases, the ML model performed better than the TL model, particularly under low data availability conditions. The better performance of the ML model compared to that of the TL model in the presence of EN may be attributed to the distinct frequency-domain pattern of EN (Figure 5j). Since the ML model processes features extracted directly from the frequency domain after dimensionality reduction, this pattern might remain prominent in the input data, allowing the model to leverage it for classification. In contrast, the TL model transforms frequency-domain data into a common latent space to align the source and target distributions. This transformation may obscure or weaken the distinct characteristics of EN, making it less distinguishable and potentially reducing the classification performance. Nevertheless, since EN are easy to detect and remove through signal processing, these cases are not critical from a practical standpoint. The weakest ML performance was observed in scenarios with strong WGN and CN, as highlighted by the light blue circles in Figure 7. The TL model significantly enhances the classification accuracy in these cases. Notably, the ML and TL models exhibit nearly identical performances for the WGN and CN. This finding was unexpected, as CN, which combines multiple noise types, was supposed to have a more substantial negative impact. IN has a minimal effect on both ML and TL models compared to the baseline performance, particularly in its weak form when data availability is high. When 100% of the data are available, the TL model handles even strong IN effectively, whereas the ML model experiences a significant drop in accuracy. Data availability plays a crucial role in TL model performance across all noise types, with increased data significantly improving the accuracy. Examples of this trend are highlighted by green arrows in Figure 7 CN. In contrast, the ML model remains largely unaffected by data availability, maintaining stable accuracy across different data volumes, as shown by the yellow horizontal lines in the same subfigure. This indicates that, in most scenarios, the TL model can achieve an accuracy comparable to that of the ML model with significantly less data. This advantage highlights the ability of the TL model to leverage domain adaptation for improved generalization, making it a viable approach in data-limited industrial applications. Figure 7: Classification accuracies of ML and TL models for each investigated scenario, grouped by noise type. Furthermore, the results suggest that the accuracy improvement of the ML model plateaued at a data availability level below 10%, indicating that additional data beyond this threshold did not contribute to further performance gains. Conversely, the TL accuracy continued to improve at 100% data availability, implying that TL models may achieve even higher accuracy with greater data availability. The impact of noise severity on the model performance is evident across most noise types, where stronger noise generally leads to lower accuracy. However, EN is an exception, as model performance remains unaffected by the increase in noise levels.