## 3 Results
### Illustrative Case Comparison of Diagnostic Frameworks  
This section presents a comparative analysis of selected fault detection and diagnosis studies in power plant systems. The examples reflect methodological diversity and illustrate varying degrees of diagnostic pipeline completeness, algorithmic sophistication, and real-world applicability.  
Case A: Full Diagnostic Pipeline with Hybrid Modeling  
[PERSON] et al. [31] proposed a hybrid framework combining Long Short-Term Memory (LSTM) networks with rule-based expert systems for fault detection and localization in nuclear power plants. The model integrated data-driven learning with symbolic reasoning to enhance both predictive accuracy and interpretability. Validation on operational datasets confirmed its coverage of the complete diagnostic chain--from detection to resolution--highlighting its implementation readiness.  
Case B: Detection-Oriented Approach with Algorithmic Innovation  
[PERSON] et al. [41] developed a CNN-based diagnostic system for detecting sensor faults in district heating substations using thermal image data. The model demonstrated high accuracy in experimental conditions but was limited to anomaly detection without incorporating localization or corrective mechanisms. Its strengths lie in algorithmic precision, although its scope is confined to early-stage diagnostics.  
Case C: Traditional Method with Narrow Pipeline Scope  
[PERSON] et al. [54] employed wavelet transform techniques for vibration-based fault detection in boiler tubes. The approach, representative of classical signal processing methods, offered computational simplicity and interpretability. However, it lacked real-world validation and did not extend beyond basic detection, thus limiting its applicability in complex diagnostic scenarios.  
Case D: Fuzzy Logic-Based Method with Limited Validation  
Figure 9: Operational flowchart for classifying fault detection studies using the proposed taxonomy.  
[PERSON] et al. [99] presented a fuzzy inference system for transformer fault classification. The method benefits from high interpretability and low computational requirements, making it suitable for real-time applications. Nonetheless, the study did not validate the approach on industrial datasets, and its scope remained confined to classification accuracy metrics, with no exploration of system-level diagnostics.  
Case E: Deep Learning in PV Systems with Partial Coverage  
[PERSON] et al. [155] applied deep learning algorithms to detect faults in photovoltaic arrays. Their model focused on classifying multiple types of PV faults with good performance under simulation conditions. However, like many DL-based approaches, the study did not address localization or suggest integration with real-time controllers, limiting its immediate deployment potential.  
Case F: Hybrid Approach without Full Pipeline Closure  
[PERSON] et al. [142] proposed a hybrid diagnostic system using a combination of fuzzy logic and machine learning classifiers to detect faults in gas turbine engines. While the conceptual design showed promise, the system lacked rigorous validation and did not fully connect detection with resolution strategies. As such, it remains at a prototype level.  
These cases collectively reinforce the observations from Sections 3.6 and 3.7: fault detection is extensively covered in the literature, but comprehensive diagnostic pipelines--from detection through to resolution--are still rare. Furthermore, the comparison underscores the trade-offs between methodological complexity, system transparency, and validation under realistic conditions.