## 1 Introduction  
The identification of degradation root causes in photovoltaic (PV) cells is critical for improving design efficiency and optimizing maintenance by targeting specific degradation mechanisms, thus reducing time and cost. However, developing such approaches faces challenges: limited instrumentation (typically voltage, current, ambient, and cell temperature sensors) and incomplete datasets, often restricted to normal operation with sparse faulty data [1, 2, 3].  
Recent reviews [3, 4] highlight that data-driven methods, particularly artificial intelligence-based techniques, excel in fault detection, achieving over 90% accuracy when comprehensive datasets are available [4]. Common targets include electrical faults (e.g., short circuits) addressed by shallow neural networks and visible faults (e.g., shading) by deep neural networks like Multi-layer Perceptrons and Convolutional Neural Networks [4]. Multivariate techniques, such as Principal Component Analysis (PCA) with Support Vector Machines (SVM), also succeed in detecting partial shading and disconnections [5]. Yet, real-world datasets often lack coverage of material-level degradation (e.g., Light Induced Degradation (LID), micro-cracks), limiting diagnostic scope.  
To address this, empirical models of degradation phenomena--e.g., LID [6], Potential Induced Degradation (PID) [7], micro-cracks [8], corrosion [9], and Ethylene-Vinyl Acetate (EVA) discoloration [10]--have been developed from lab data. These models, while representative, are rarely used online due to their disconnect from measurable variables.  
The identification of degradation mechanisms in PV systems has therefore gained renewed interest, especially with the advancement of digital twin frameworks and intelligent fault detection techniques. However, most existing solutions rely heavily on either black-box data-driven models that lack interpretability or overly simplified physical models that cannot fully capture real-world fault complexity. Notable efforts to analyze degradation mechanisms include studies by [PERSON] et al. [9], [PERSON] et al. [11], and [PERSON] [12], though these often target specific faults in isolation rather than providing a generalizable solution.  
In this paper, we propose a reproducible and modular methodology that integrates physical modeling with empirical degradation behaviors and machine learning-based classification. A generic PV cell simulator is developed using bond graph methodology [13], capturing electro-thermal dynamics and supporting degradation embedding. This enables the generation of synthetic, labeled datasets under varied fault conditions. An optimized Feed-Forward Neural Network (OFFNN) is trained on this dataset, using Relieff feature selection [14] to identify the most informative inputs. This hybrid simulation-classification framework promotes scalability, interpretability, and open reproducibility, aligning with recent developments in smart energy management and automation in PV systems [15].  
The remainder of the paper is structured as follows: Section 2 presents the modeling approach, Section 3 describes the detection algorithm, Section 4 analyzes the results, and Section 5 concludes with future research directions.