## 4 Results and Discussion
### Class-Wise Performance, Dataset Limitations, and Interpretability  
While Class 1, representing Normal, shows high performance due to the availability of real data, the model also performs well in distinguishing other degradation types based on synthetic data. Classes 3 and 4 (e.g., EVA discoloration and cracks) show F1-scores above 0.74, indicating that their impact on the measured signals is distinctive and detectable by the trained network.  
Class 5, representing LID, shows the lowest precision (0.68) and F1-score (0.69). This may be attributed to the subtle signal changes induced by LID in early stages, which overlap with other degradations in the feature space. Further improvement could be achieved by enhancing feature extraction techniques or including more detailed thermal-electrical behavior modeling for LID.  
These insights confirm that, while some degradation modes are clearly identifiable, others may require advanced preprocessing or more sensitive measurement parameters. Nonetheless, the model demonstrates the ability to generalize from both real and simulated data, highlighting its potential for practical deployment in PV monitoring systems.  
While the simulation-based dataset allows for detailed control over fault scenarios and environmental conditions, we acknowledge that the model's validation is limited by the absence of real-world degraded PV data. This may affect the immediate practical applicability of the classifier, particularly in field conditions where sensor noise, calibration drift, and complex environmental dynamics are present. The current study is intended as a reproducible and modular foundation. Future extensions will aim to incorporate experimental datasets and field measurements to improve robustness and ensure the classifier generalizes effectively to real operational settings.  
Another important limitation is the exclusive use of single-fault degradation scenarios in the simulation process. In practice, photovoltaic systems often experience simultaneous or interacting degradation mechanisms, which can result in more complex and ambiguous signal patterns. The current model does not yet address such compound fault scenarios. Furthermore, real sensor measurements are subject to noise, environmental variability, and hardware aging--factors not captured in the clean, synthetic dataset. These issues may reduce the classifier's robustness and accuracy in field applications. Future work will focus on simulating multi-fault conditions and introducing realistic noise models to better emulate operational environments and improve fault differentiation under non-ideal conditions.  
It is also important to distinguish between the interpretability of the physical simulation layer and the neural network classifier. While the bond graph-based degradation modeling enables physically meaningful and transparent feature generation, the OFFNN component, like many deep learning models, functions as a black box. Although feature selection via Relief improves understanding of input relevance, the internal decision logic of the network remains opaque. Nevertheless, the use of physically meaningful simulation inputs and feature ranking helps bridge the gap between black-box predictions and physical interpretability, providing partial transparency into the classification process. Future extensions could explore the integration of explainable AI (XAI) techniques such as SHAP values [30] or LIME [31] to provide post-hoc insights into model predictions. Alternatively, inherently interpretable models like decision trees or rule-based classifiers may offer greater transparency [32].