## 4 Discussion
### Recommendations for Future Research  
Based on the extensive review and analysis conducted in this study, several strategic directions can be identified to guide future research efforts in the domain of fault detection and diagnosis (FDD) for power plant systems. These recommendations stem from observed methodological patterns, identified research gaps, and emerging industry needs.  
a. Prioritize Full-Process Diagnostic Frameworks  
As noted in Section 3.5, a large proportion of studies focus exclusively on fault detection, with limited attention to localization and intervention. Future research should prioritize the development of end-to-end frameworks that encompass the following:  
* Detection of anomalies or abnormal patterns;
* Localization and identification of root causes;
* Proposal and simulation of corrective actions.  
Such integrated pipelines are essential for real-time implementation and practical decision support.  
b. Develop Hybrid and Physics-Informed Learning Models  
Although deep learning has dominated recent publications (Section 3.8), it remains largely decoupled from the physical principles governing power plant behavior. Future work should invest in the following:  
* Hybrid architectures that embed physical constraints into data-driven learning;
* Physics-informed neural networks that balance model flexibility with interpretability;
* Cross-validation with real industrial data to enhance robustness and generalization.  
c. Explore Cross-Domain Fault Propagation  
Most reviewed articles examine faults in isolated systems (boilers, turbines, etc.). However, real-world failures often propagate across mechanical, electrical, and control domains. New models should account for the following:  
* Multidomain coupling (e.g., thermal-electrical interactions);
* Temporal propagation and delay effects;
* Networked diagnosis, integrating data from multiple subsystems.  
This is particularly relevant in hybrid and smart grid environments where dynamic interconnections prevail.  
d. Address Interpretability and Explainability in AI Models  
With the increasing adoption of black-box models (e.g., CNNs, LSTMs), transparency becomes a critical barrier to deployment in high-risk environments. Future research should incorporate the following:  
* Post hoc interpretability tools (e.g., SHAP, LIME);
* Ante-hoc interpretable models (e.g., decision trees, symbolic regression);
* Operator-in-the-loop systems that combine human expertise with automated diagnosis.  
e. Standardize Datasets and Evaluation Protocols  
The lack of open, shared datasets hampers replication and comparative benchmarking.  
The community would benefit from the following:  
* Public repositories for annotated sensor data;
* Benchmark challenges similar to those in computer vision or NLP;
* Agreement on key performance metrics (e.g., accuracy, latency, explainability, computational load).  
f. Bridge Academia and Industry through Pilot Deployments  
Very few studies included results from live deployments or pilot programs in operational power plants. Research initiatives should aim for the following:  
* Field validation of diagnostic frameworks;
* Collaborative trials with utilities and plant operators;
* Iterative feedback from users to refine system usability and alerts.  
This transition from academic modeling to industrial practice is essential for creating tangible impact.  
Despite its breadth and methodological rigor, this review has certain limitations. The inclusion criteria were limited to articles indexed in MDPI, Elsevier, and Google Scholar, which, although comprehensive, may exclude proprietary industrial reports, patents, or white papers not publicly accessible. Furthermore, the article classification and coding were conducted manually, which--while thorough--may involve subjective interpretation. These limitations do not affect the validity of the findings but should be considered when generalizing the results.