## 1 Introduction
Thermal power plants, based on fossil fuel combustion, are still the most widely studied systems in the literature, primarily due to their complex thermodynamic cycles and vulnerability to critical failures in boilers, turbines, and heat exchangers [33; 34]. In these plants, sensor-based monitoring--particularly temperature, pressure, and vibration--is fundamental to prevent catastrophic failures such as tube ruptures, steam leaks, and thermal fatigue [35; 36]. While structurally similar to thermal systems in terms of steam generation and tur-bine use, nuclear power plants introduce additional constraints related to safety, redundancy, and fault containment. Diagnostic systems in nuclear facilities often require higher robustness, fault tolerance, and explainability, as decision-making must adhere to strict regulatory frameworks [37; 38]. Many nuclear-related FDD studies focus on essential service water pumps, feedwater systems, and temperature control units [39]. Though fundamentally different in architecture, photovoltaic (PV) and hybrid renewable systems have gained increasing attention in recent years. Faults such as panel degradation, hot spots, or diode failure in PV arrays are typically identified using infrared thermography, drone-based inspection, or current-voltage (I-V) curve analysis [40; 41]. The decentralized nature of these systems, along with their distributed control logic, poses challenges for system-level diagnosis, especially in the absence of centralized monitoring hubs [42]. Combined-cycle and hybrid plants, which integrate gas turbines with steam generators and/or renewable sources, represent another growing area of research. These systems are more efficient but complex, often involving interactions between combustion dynamics, heat recovery mechanisms, and electrical load balancing [43; 44]. Fault diagnosis in such settings demands a hybrid approach--both in terms of sensing modalities (e.g., acoustic, thermal, electrical) and analytical models. As observed in recent review efforts, the diagnostic needs, sensor deployment strategies, and data acquisition requirements differ greatly across these plant types [45; 46; 47]. Therefore, FDD models must be tailored to the fault mechanisms and the system architecture, operating environment, and data granularity. This complexity underlines the need for adaptable, modular, and context-aware diagnostic frameworks that can be customized to different energy systems. Modern fault detection and diagnosis systems rely heavily on sensor networks, which form the foundation of data-driven maintenance and intelligent monitoring. The choice of sensor type, placement, sampling frequency, and integration with control systems profoundly influences the quality and reliability of diagnostic outputs [48; 49]. In thermal and nuclear power plants, the most common sensors include the following:  
* Temperature sensors (RTDs, thermocouples)--essential for steam cycle regulation, heat exchanger monitoring, and anomaly detection in turbines or superheaters;
* Pressure transducers--used for monitoring boiler conditions, valve operations, and fluid flow dynamics;
* Vibration sensors (accelerometers, velocity pickups)--widely employed for rotating machinery, such as pumps, fans, and compressors, to detect imbalance, misalignment, and bearing degradation [50; 51; 52]. Acoustic sensors are increasingly used in hydroelectric and combined-cycle plants for detecting cavitation, flow disturbances, and mechanical anomalies in turbines or pipe systems [53]. Similarly, infrared thermography has become a standard tool in PV installations and electronic control unit diagnostics, enabling non-invasive detection of over-heating, diode faults, or insulation breakdowns [54]. Recent years have also witnessed a transition from single-point sensing to multi-sensor fusion, where data from different modalities (e.g., thermal + acoustic + electrical) are combined to improve diagnostic accuracy [55].