## 3 Results
### Proposed Taxonomy of Diagnostic Systems  
To synthesize the diverse findings of this review and provide a structured reference for future research, we propose a taxonomy of fault detection systems in power generation contexts. This taxonomy is based on four key dimensions observed across the literature:  
Figure 8: Distribution of algorithm types by source. (1) the type of power plant analyzed, (2) the type of sensor technology employed, (3) the algorithmic approach used, and (4) the level of diagnostic pipeline. Table 4 presents a visual matrix summarizing the most frequently observed combinations among these categories. The matrix includes entries from thermal, nuclear, PV, gas, and hybrid systems, each associated with specific sensor types (e.g., vibration, infrared, thermal imaging), algorithmic methods (e.g., traditional, machine learning, deep learning, hybrid), and degrees of diagnostic pipeline--from simple detection to full pipeline execution. Several insights emerge from this structured view:  
* Thermal power plants remain the dominant application context, often monitored through vibration or temperature sensors;
* PV systems tend to utilize thermal imaging and deep learning techniques, reflecting advances in remote, image-based fault detection;
* Nuclear and hydroelectric facilities employ pressure or acoustic sensors, typically analyzed via traditional or ML-based approaches;
* Hybrid and gas systems are associated with more complex data sources and tend to incorporate hybrid algorithms with higher process integration. This taxonomy serves a dual purpose: it captures the current state of the literature and offers a framework for positioning future studies. Researchers may use it to identify underexplored combinations (e.g., hybrid systems with full-process DL-based coverage) or to align their methodology with typical system-sensor-algorithm pairings. Moreover, this classification model facilitates comparative analysis across publications and can support the development of standardized evaluation benchmarks for fault detection frameworks in power generation. The observed combinations in Table 4 reflect both technological maturity and system-specific requirements. For example, thermal power plants are predominantly monitored using vibration and temperature sensors due to the presence of rotating equipment (e.g., turbines, pumps) and thermodynamic cycles. These data types are well-suited for traditional algorithms such as Principal Component Analysis (PCA) and Fault Detection and Isolation (FDI), which remain favored in legacy monitoring architectures. In contrast, photovoltaic (PV) systems often rely on infrared imaging and benefit from deep learning (DL) models capable of extracting fault patterns from visual data. The taxonomy allows researchers to identify underrepresented combinations (e.g., hybrid algorithms applied to system-level diagnostics in hydroelectric systems) and optimize diagnostic system design by aligning sensor types, algorithmic complexity, and coverage scope. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Power Plant Type** & **Sensor Type** & **Algorithm** & **Diagnostic Pipeline** & **No. of Articles** \\ \hline \multirow{3}{*}{Thermal} & Vibration & Traditional & Detection Only & 18 \\  & Temperature & Machine Learning & Detection + Localization & 15 \\  & Current & Deep Learning & Full Pipeline & 7 \\ \hline \multirow{3}{*}{PV} & \multirow{3}{*}{Thermal Imaging} & \multirow{3}{*}{Deep Learning} & Full Pipeline & 12 \\ \cline{3-5}  & & & Detection + Localization & 6 \\ \hline Nuclear & Pressure & Traditional & Detection Only & 10 \\ \hline Hybrid & Multisensor & Hybrid & Detection + Localization & 9 \\ \hline Hydroelectric & Acoustic & Machine Learning & Detection Only & 8 \\ \hline Gas & Infrared & Hybrid & Full Pipeline & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Structured Taxonomy of Reviewed Articles (Summary of Most Frequent Combinations).