# Lab-to-Field Generalization Gap: Assessment of Transfer Learning for Bearing Fault Detection
## 4 Conclusions  
This study evaluated the ability of TL models to address the generalization challenges of traditional ML models when transferring AI models trained on lab-recorded data to an emulated workshop environment with varying operational conditions. The case study focuses on detecting rotor unbalance across different speed variations and provides insights into the strengths and limitations of traditional ML and TL models. The classification accuracy, sensitivity, and specificity were analyzed across multiple scenarios, considering the effects of data availability, noise type and severity, rotational speed variations, and different unbalance levels. The results demonstrate that the TL model outperforms the ML model, particularly in source-to-target tasks with high data availability, achieving up to a 30% accuracy improvement in complex noise environments such as WGN and CN. While ML effectively detects unbalanced conditions, it often misclassifies balanced cases. TL maintains strong unbalance detection but struggles with mild unbalance levels, particularly under limited data conditions. The ML accuracy remains stable across different data availability levels, whereas TL showed continued improvement with more data. This study also highlights the influence of noise type, severity, and speed variations. The WGN and CN presented the greatest challenges for both models, but TL exhibits greater robustness. ML benefits from higher speeds achieving better classification results for high RPM samples, while TL remains stable across different speed conditions, with accuracy primarily dependent on unbalance levels rather than speed. The ML accuracy improvement curve plateaus below 10% data availability, with no further gains beyond this threshold. In contrast, TL accuracy continued to improve at 100% data availability, suggesting the potential for even higher performance with more data. These findings emphasize the ability of the TL to generalize to noisy environments and adapt to complex conditions, reinforcing its potential for real industrial applications. It is important to highlight that classification accuracy alone is an insufficient metric for assessing real-world effectiveness. As demonstrated in this study, a comprehensive evaluation of model performance is crucial for enhancing the reliability and improving the diagnostic capabilities of TL models. In terms of practical application, the proposed method is computationally efficient and suitable for real-time analysis using 1-second data segments, making it feasible for industrial settings where continuous monitoring may be constrained by operational limitations or sensor accessibility. However, the current model was trained on a single bearing geometry and fault type, which limits its generalizability to other use cases. To develop a broadly applicable system, future work should incorporate data from diverse bearing types and failure modes. While this study did not aim to build a general-purpose diagnostic tool, it provides a structured analysis of how key parameters influence TL performance. The presented results were based on a case study of rotor unbalance identification through bearing vibration signals. However, the insights gained from this study can be used beyond this specific application. The findings on TL's effectiveness and limitations in handling domain shift may offer valuable guidance for investigating other bearing failure modes, such as inner and outer race defects or rolling element damage. Additionally, these insights can contribute to more detailed studies on fault detection in other rotating machinery applications, including gearbox failures, motor faults, and similar industrial maintenance tasks where data scarcity and domain adaptation remain significant challenges. The proposed methodology can be applied to assess the impact of other influencing factors, such as outliers in the data and variations in the sensor placement, which affect the recorded data characteristics.