## 1 Introduction  
Power generation systems are among modern society's most complex and mission-critical infrastructures. Whether operating on thermal, nuclear, renewable, or hybrid principles, these systems are continuously exposed to internal stresses, environmental conditions, and operational wear that can lead to unexpected faults [1, 2]. Mechanical degradation, sensor failure, and control loop disruptions can result in significant lossesdue to unplanned shutdowns, equipment damage, and safety risks [3; 4; 5]. In particular, component-level defects--such as rotor imbalance, shaft misalignment, or superheater corrosion--are frequently cited as the root causes of large-scale incidents and long-term inefficiencies in power plants [6; 7; 8]. The economic and operational consequences of undetected faults are substantial. Studies have estimated that unscheduled outages due to equipment failure can incur losses ranging from hundreds of thousands to millions of dollars per day, depending on the plant size and energy market [9; 10]. As a result, fault detection and diagnosis (FDD) has emerged as a strategic priority for plant operators, utilities, and equipment manufacturers. FDD systems aim to identify anomalies in sensor readings or process behavior and localize their origin, assess severity, and recommend corrective actions [11; 12; 13]. Over the last two decades, the evolution of fault detection and diagnosis (FDD) systems has been tightly linked to advancements in sensing technologies, data acquisition systems, and algorithmic intelligence. Traditional approaches--often rooted in signal thresholding, rule-based logic, or model-based residual generation--remain used for well-characterized processes such as boiler pressure control or turbine vibration monitoring [14; 15; 16]. However, these methods typically require accurate physical models and are limited in adaptability to novel fault patterns, noisy signals, or multi-source disturbances [17; 18]. In contrast, recent developments in machine learning (ML) and artificial intelligence (AI) have transformed the landscape of fault diagnostics. Algorithms such as Support Vector Machines (SVM), Decision Trees (DT), Random Forests (RF), and k-Nearest Neighbors (k-NN) have been widely applied for anomaly classification, fault severity estimation, and condition monitoring [19; 20; 21; 22]. These methods offer greater flexibility, especially when large amounts of labeled or unlabeled data are available, but they also introduce challenges regarding model generalization, interpretability, and domain adaptation. Integrating deep learning (DL) into FDD has further expanded the horizon of diagnostic capabilities. Architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs) have shown remarkable performance in handling complex, high-dimensional data from industrial plants [23; 24; 25]. These models are particularly suited for analyzing temporal sequences, vibration signals, and thermographic imagery, often outperforming classical methods in fault detection sensitivity and robustness to noise [26; 27]. Despite these promising developments, several factors have limited the widespread adoption of AI-based FDD systems in power plants. These include the scarcity of high-quality, labeled datasets, lack of domain-specific model interpretability, and difficulty integrating data-driven insights into real-time decision support systems [28; 29; 30]. Moreover, most studies in the literature focus on component-level diagnoses--such as bearings, heat exchangers, or inverters--while relatively few address full-system be-haviour, cross-domain interactions, or plant-wide fault propagation mechanisms [31; 32]. Power plants' architecture and operational characteristics vary significantly depending on the energy conversion method and generation technology.