## 4 Discussion  
### Research Gaps Identified in the Literature  
The results presented in this review highlight significant progress in the field of fault detection and diagnosis (FDD) for power plant systems. However, beyond the breadth of methodologies and application areas covered, several critical gaps persist in the current body of literature. Identifying these gaps is essential not only for contextualizing existing research but also for guiding future investigations toward more complete, validated, and applicable frameworks. First, although machine learning and deep learning approaches have become increasingly dominant--accounting for nearly 70% of all algorithmic implementations reviewed--there is a clear underrepresentation of hybrid models that effectively combine data-driven learning with domain-specific physical modeling. While a few articles proposed such integrations (e.g., combining CNNs with fuzzy logic or applying LSTM within physics-informed networks), these remain isolated examples and are often limited to simulation environments rather than industrial validation. Second, fault propagation across interconnected subsystems--such as from thermal to electrical or control domains--is rarely explored in depth. Most studies focus on isolated components (e.g., boilers, turbines, inverters), often without considering how localized faults might influence or cascade across other parts of the system. This siloed view limits the ability of current FDD frameworks to support system-level resilience and optimization. Third, there is a lack of empirical validation in real-world conditions. Despite the methodological rigor of many papers, only a minority presented results derived from actual industrial systems or high-fidelity testbeds. For instance, only a few articles employed field data from operating nuclear, hybrid, or wind plants (e.g., [17; 100]), and even fewer incorporated uncertainty, noise, or missing values into their evaluation protocols. Fourth, data availability and standardization remain unresolved issues. A large proportion of articles relied on custom datasets, unpublished simulations, or inaccessible sources, making comparative evaluation difficult. The absence of shared benchmarks impairs reproducibility and slows down algorithmic improvement across research groups. Fifth, while detection is thoroughly addressed, localization and corrective action are often neglected. As shown in Section 3.5, less than one-third of the reviewed articles addressed the full process pipeline. Moreover, the logical link between diagnosis and decision support (e.g., triggering a shutdown, rerouting load, or initiating predictive maintenance) is frequently missing or left as future work. Finally, interpretability and explainability are rarely discussed, especially in studies applying complex deep learning models. In safety-critical infrastructure like power generation, this gap poses serious limitations to trust, deployment, and compliance with operational standards. Explainability approaches in FDD research are often categorized as post hoc or ante-hoc. Post hoc methods, such as SHAP values or saliency maps, are applied after model training to interpret the output of complex models. Ante-hoc methods, in contrast, are interpretable by design--for example, decision trees, linear models, or symbolic reasoning frameworks. This distinction is widely accepted in the interpretability literature and is critical for balancing predictive performance with transparency in high-risk applications such as power system diagnostics. These research gaps point to the need for more integrative, validated, and interdisciplinary approaches that go beyond methodological innovation to address implementation, transparency, and industrial relevance. Table 5 presents a comparative overview of the key advantages and limitations observed across the main categories of fault detection methodologies discussed in this review.