## 4 Discussion
### Methodological Trends and Technological Shifts  
The past decade has seen a profound shift in the methodological landscape of fault detection and diagnosis (FDD) for power plant systems. As detailed in Section 3.9 and illustrated in Figure 5, the literature reveals a transition from classical statistical approaches to increasingly complex and adaptive machine learning models, with deep learning now representing the methodological core of many recent studies. Early works--especially those prior to 2018--relied heavily on rule-based systems, principal component analysis (PCA), and model-based fault detection and isolation (FDI) techniques. These approaches, though interpretable and computationally efficient, often struggled with nonlinear system dynamics, sensor noise, and the high dimensionality of real-time industrial data. Nonetheless, they provided foundational tools that are still relevant for interpretable fault modeling and rapid prototyping in simpler systems [125]. For instance, [PERSON] in [54] demonstrated the use of decision tree-based classifiers in the detection of early-stage boiler tube failures, while [PERSON] in [119] and [PERSON] in [153] applied SVM and ensemble models to classify vibration anomalies in turbine systems. Hybrid models integrating fuzzy logic and neural networks, as presented by [PERSON] in [134] and [PERSON] in [71], remain underrepresented despite their promising performance in simulation-based environments. Between 2018 and 2021, there was a surge in the adoption of shallow learning algorithms, such as Support Vector Machines (SVM), Random Forests (RF), and k-Nearest Neighbors (k-NN). These models offered higher flexibility and improved classification accuracy, particularly when dealing with labeled datasets derived from vibration or thermal sensors. They became the method of choice for many component-level diagnosis tasks, including rotor imbalance, thermal stress monitoring, and converter anomalies [73]. From 2021 onward, the field has increasingly embraced deep learning (DL), with Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs) emerging as dominant tools for analyzing high-frequency signals, multivariate time series, and even thermal imagery [15; 75]. These models demonstrated strong generalization ability, robustness to noise, and end-to-end learning capacity, especially in large-scale or sensor-rich environments. However, their adoption has been tempered by concerns regarding data requirements, interpretability, and computational cost. In parallel, a small but growing subset of studies introduced hybrid architectures, combining the physical interpretability of model-based systems with the predictive power of data-driven methods. Examples include fuzzy logic-enhanced neural networks, physics-informed neural differential models, and multistage architectures incorporating both supervised learning and rule-based filtering [77]. Despite their potential, these hybrid methods remain underexplored, partly due to the technical complexity of integrating physical constraints into learning workflows. Another notable trend is the increasing use of multisensor fusion and unsupervised anomaly detection, particularly in smart grid environments and hybrid power generation systems. These methods often leverage autoencoders, clustering algorithms, or dimensionality reduction techniques to extract fault patterns without prior labels--a valuable feature in data-scarce or dynamically evolving settings [129]. Overall, the methodological evolution of the field reflects both technological advancements in sensor systems and growing computational capabilities, as well as a broader conceptual shift: from offline diagnosis toward real-time, adaptive, and decision-integrated fault management solutions. Recent years have seen a pronounced shift in research trends, with a growing focus on machine learning (ML) and deep learning (DL) techniques tailored to power electronics systems. Studies such as [PERSON]'s in [96] and [PERSON] et al.