<![CDATA[The long-term reliability of photovoltaic (PV) systems depends on the timely detection and diagnosis of faults. Crystalline silicon (c-Si) technology dominates the global PV market, and is susceptible to a wide range of degradation modes. This review provides a structured analysis of these faults, categorizing them into material-level intrinsic defects, environmentally-induced extrinsic faults, and system-level interconnection faults. The review details the underlying mechanisms of key degradation modes, including Light- and Elevated Temperature-Induced Degradation (LeTID), Potential Induced Degradation (PID), and micro-crack propagation. A critical evaluation of corresponding Fault Detection and Diagnostic (FDD) methodologies follows. It encompasses laboratory-grade imaging techniques, field-deployable electrical analysis, and emerging data-driven approaches leveraging machine learning and unmanned aerial vehicles (UAVs). This synthesis reveals a fundamental trade-off between diagnostic resolution and operational scalability. To navigate this trade-off, the study analyzes the evolution towards integrated, tiered monitoring strategies and hybrid data-fusion techniques. Furthermore, the review identifies persistent research gaps, such as the need for explainable artificial intelligence (XAI), standardized datasets, robust transfer learning models, and cyber-secure FDD architectures. By bridging the fundamental science of cell degradation with the system-level engineering, this article serves as a roadmap for advancing predictive maintenance and ensuring the sustainability of large-scale PV infrastructure.]]>
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