## 5 Conclusions  
This paper presents a hybrid methodology for the detection and identification of degradation root causes in photovoltaic (PV) cells by integrating bond graph-based physical modeling with empirical degradation profiles and deep learning classification. The proposed approach simulates various degradation modes using a modular and scalable framework, generating synthetic datasets to train an optimized feed-forward neural network (OFFNN). The network, enhanced by Relieff-based feature selection and Genetic Algorithm optimization, successfully classifies degradation types with 75.43% accuracy, demonstrating robustness even in scenarios with limited real-world fault data.  
The results confirm the feasibility of combining physical simulations with AI to enable accurate root-cause diagnostics using only standard PV sensor data. The interpretability and modularity of the method make it well-suited for deployment in intelligent PV monitoring and predictive maintenance systems.  
Future research will focus on extending the simulator to include a broader range of degradation mechanisms, such as potential-induced degradation (PID) under variable humidity and long-term voltage stress, as well as more complex physical phenomena such as delamination. These additions are expected to enhance the realism and practical relevance of the diagnostic framework. Moreover, future extensions may benefit from incorporating multi-modal diagnostic inputs, including infrared thermography, which has shown promise in PV fault detection and classification when combined with deep learning approaches [35]. Additionally, exploring temporal models (e.g., LSTM networks) may enhance the ability to track fault progression over time. Deployment on embedded systems or edge devices for real-time diagnosis is also planned to enable field-level implementation. Finally, future work will investigate noise injection techniques and the use of hybrid datasets that include real-world measurements and sensor noise profiles to improve model robustness and ensure realistic behavior under field conditions.  
Conceptualization, M.D. and N.F.; methodology, M.D. and N.F.; software, N.F.; validation, M.B., H.A.S., and N.M.; formal analysis, M.D.; investigation, M.D. and N.F.; resources, M.B.; data curation, N.F.; writing--original draft preparation, M.D.; writing--review and editing, N.F. and H.A.S.; visualization, N.F.; supervision, M.B. and N.M.; project administration, M.D.; funding acquisition, M.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Acknowledgments:** The authors would like to acknowledge the administrative support provided by Aix-Marseille University, Ankara Medipol University, City University (Tripoli), and the Lebanese University during the course of this research.  
**Conflicts of Interest:** The authors declare no conflicts of interest.