## 4 Discussion
### Practical Implications for Power Plant Monitoring  
Diagnostic systems that implement the full pipeline--from detection to localization and resolution--offer significant operational advantages. These include faster reaction times, improved integration with control systems, and the ability to automate maintenance scheduling or emergency responses. In high-stakes environments like thermal and nuclear plants, full-process frameworks contribute directly to risk reduction and cost efficiency. The results synthesized in this review have direct implications for the design, deployment, and management of diagnostic systems in power generation facilities. As the energy sector continues to evolve toward increased automation, digital integration, and sustainability, the ability to monitor and respond to equipment faults in real time becomes a critical operational requirement. One of the most significant practical takeaways from the analysis is the importance of customizing diagnostic systems to the architecture of the power plant. As shown in Section 3.1 and as detailed in the tables summarizing platform-specific plant types and sensors, different plant types--thermal, nuclear, hybrid, or photovoltaic--require distinct sensing strategies and are associated with different types of faults. For instance, vibration and temperature sensors dominate thermal systems, while thermal imaging and infrared sensing are more common in PV arrays. Selecting the appropriate combination of sensors and algorithms is, therefore, not only a technical but also an economic decision, balancing accuracy, coverage, and cost. The dominance of component-level studies (over 80%) also reveals a limitation in current deployment practices. Most real-world diagnostic systems remain constrained to single-unit monitoring (e.g., turbines, boilers, inverters), without integration into a plant-wide monitoring architecture. This fragmentation reduces the ability to detect and mitigate cascading faults, where an anomaly in one subsystem can propagate through electrical, thermal, or control pathways and impact overall system reliability. Bridging this gap will require multi-domain fault propagation models, as well as unified data architectures capable of consolidating input from heterogeneous sensors. Another implication concerns the limited availability of end-to-end diagnostic platforms. As discussed in Sections 3.5 and 3.10, only 30.3% of the reviewed studies addressed the full process--detection, localization, and resolution. This represents a missed opportunity for implementing decision-support systems, which could recommend or trigger corrective actions such as load redistribution, predictive maintenance scheduling, or system reconfiguration. Industrial operators would benefit from integrating diagnostic systems with control layers and maintenance management platforms (e.g., SCADA or CMMS), thereby shortening the feedback loop between anomaly detection and plant response. The growing reliance on deep learning models (Sections 3.3 and 3.8) introduces both potential and risk. While these models have demonstrated superior fault classification performance, their black-box nature and reliance on large, labeled datasets limit their adoption in safety-critical applications. Operators often require transparency in diagnostic outputs and the ability to audit decision logic. Thus, model selection must be balanced with operational constraints, and the inclusion of interpretable AI or hybrid models may serve as a compromise between performance and explainability. Finally, the underrepresentation of field-validated studies suggests that many diagnostic frameworks remain trapped in academic settings. For practical implementation, research efforts must increasingly incorporate the following:  
* Realistic noise levels and sensor failure scenarios;
* Variability in operating conditions;
* Integration with existing control systems;
* User-friendly interfaces and alerts.