## 3 Results
### Types of Algorithms Employed  
Articles were grouped based on the nature of their proposed methods: traditional/statistical models, shallow learning, deep learning, hybrid, and non-algorithmic. Figure 3 provides a breakdown by article type. The reviewed literature demonstrates a wide variety of algorithmic techniques utilized for fault detection, localization, and predictive modeling in power generation systems. To provide a structured overview, the algorithms were grouped into five categories: (1) traditional statistical and mathematical models, (2) shallow learning methods, (3) deep learning techniques, (4) hybrid approaches, and (5) non-algorithmic or unspecified methods. A detailed analysis shows that traditional models such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), Fuzzy Logic Systems, and model-based Fault Detection and Isolation (FDI) schemes remain foundational in many studies. These  
Figure 3: Article types. methods were particularly common in earlier publications and are often favored for their interpretability, computational efficiency, and compatibility with existing control systems. Shallow learning techniques--including Support Vector Machines (SVM), Decision Trees, and k-Nearest Neighbors (k-NN)--were found in a significant portion of the mid-range publications (2017-2021). These models offer relatively fast training times and perform well on structured datasets, which are typical in condition monitoring systems of thermal and gas power plants. Deep learning architectures, especially Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRUs), have become dominant in recent years. These models are particularly effective in handling unstructured and temporal data (e.g., vibration signals, thermal imagery, multivariate time series). The use of these architectures reflects the increasing availability of sensor-rich environments and high-volume data streams in modern power plant infrastructures. Hybrid models, combining physical-based modeling with machine learning (so-called model-based + data-driven strategies), represent an emerging direction. While still under-represented in the dataset, these approaches offer promising results by integrating physical knowledge into flexible learning frameworks. This is especially useful in systems where explainability, robustness, and real-time applicability are crucial. Finally, a small fraction of articles did not specify the algorithm used or relied purely on theoretical or qualitative frameworks. This trend is declining as the field matures and methodological rigor becomes an expected standard. In the domain of power electronics, ML-based approaches have shown particular promise in the detection of faults in converters and switching devices. For instance, [PERSON] in [42] introduces a hybrid CNN-RNN architecture optimized for power module diagnostics, while [48] employs an attention-based classifier for real-time failure prediction in inverter systems. These methods offer high precision under high-speed operating conditions and represent an emerging research direction within the broader FDD landscape. While both ML and DL techniques are prevalent in FDD systems, they differ substantially in scope and implementation. ML methods such as SVM and Random Forests are suitable for structured, low-dimensional datasets and offer relatively high interpretability and low computational cost. In contrast, DL models (e.g., CNN, LSTM) excel in analyzing unstructured and high-dimensional data such as thermal images or multivariate time series but require larger training datasets and suffer from lower explainability. The choice between the two often hinges on data availability, real-time constraints, and the desired balance between accuracy and transparency.