## 5 Conclusions  
This review analyzed 185 peer-reviewed articles focused on fault detection and diagnosis (FDD) in power plant systems, spanning the period 2015-2024 and sourced from MDPI, Elsevier, and Google Scholar. The study classified contributions by algorithmic methodology, plant type, sensor technology, publication year, and diagnostic pipeline.  
The results reveal a clear shift from traditional diagnostic techniques to machine learning (ML) and deep learning (DL) models, with over 70% of articles published in the last five years adopting AI-driven approaches. However, the field remains largely fragmented--most studies address only detection, lack full pipeline integration, and focus on component-level faults rather than system-wide diagnostics. Real-world validation, hybrid model adoption, and integration with control systems remain underdeveloped.  
To address these limitations, this article introduces a structured taxonomy of diagnostic configurations, a summary of key findings, and a methodological checklist for assessing study completeness. These tools aim to support standardization, comparability, and practical implementation of diagnostic frameworks.  
Future research should focus on bridging gaps between algorithmic performance and operational deployment, by emphasizing transparency, cross-domain modeling, and validation under realistic conditions. The synthesis provided in this review serves not only as a snapshot of current practices but also as a foundation for designing next-generation diagnostic systems in the power generation sector.  
Conceptualization, C.A.M., V.M.R. and C.F.P.; methodology, C.A.M.; software, C.A.M. and V.M.R.; validation, C.A.M., D.M.P.-P. and C.F.P.; formal analysis, C.A.M.; investigation, C.A.M. and V.M.R.; resources, V.M.R., D.M.P.-P. and C.F.P.; data curation, C.A.M.; writing--original draft preparation, C.A.M. and V.M.R.; writing--review and editing, D.M.P.-P. and C.F.P.; visualization, C.F.P.; supervision, C.A.M.; project administration, C.A.M., V.M.R. and C.F.P.; funding acquisition, V.M.R. and C.F.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Criterion** & **Importance** & **Coverage in** & **Typical Gaps** \\  & **Level** & **Literature (\%)** & **Cyperial Gaps** \\ \hline
1. Clearly defines fault types and & & & \\ system context & Critical & High (\textgreater{}85\%) & **Cygue fault categorization or plant setup** \\
2. Applies a validated diagnostic algorithm & Critical & High (\textgreater{}90\%) & No empirical or simulated validation \\
3. Includes fault localization capability & Moderate & Medium (\textgreater{}45\%) & **Detection only, without spatial analysis** \\
4. Proposes a corrective or decision action & Critical & Medium (\textgreater{}69\%) & No link to control or \\  & & & maintenance strategy \\
5. Validated on real or realistic data & Essential & Low (\textgreater{}25–30\%) & Synthetic/simulated data only \\
6. Addresses sensor uncertainty and noise & Important & Low & Assumes ideal data conditions \\
7. Ensures model interpretability & Important & Low & Uses black-box models \\ or transparency & & & without explanation \\ \hline \hline \end{tabular}
\end{table}
Table 6: Checklist for Evaluating Methodological Completeness in FDD Studies.