## 1 Introduction
This approach is especially beneficial in complex systems where no single measurement fully captures the fault signature. Techniques such as Kalman filtering, Dempster-Shafer theory, and sensor redundancy models have been applied to fuse data from heterogeneous sources [56; 57]. Equally important is the architecture of data acquisition and processing. While legacy plants often rely on SCADA (Supervisory Control and Data Acquisition) systems with limited bandwidth and centralized control, modern facilities increasingly employ edge computing, wireless sensor networks (WSNs), and cloud-based analytics for real-time decision-making [58; 59]. These advancements enable the deployment of AI-based FDD systems capable of autonomous learning, adaptation to process drift, and anomaly prediction. However, despite the growing availability of high-resolution sensor data, data quality remains a major challenge. Signal noise, sensor drift, missing values, and mislabeling can severely affect model performance and decision reliability [60]. Therefore, pre-processing techniques such as denoising, imputation, and signal validation are indispensable components of any FDD pipeline. While data-driven fault detection and diagnosis methods have shown great promise in experimental and simulated environments, several practical challenges limit their adoption in real-world industrial settings. One of the most persistent issues is the lack of labeled fault data. Power plants, especially those in critical infrastructure domains such as nuclear or combined-cycle generation, experience faults infrequently--yet when they occur, they are often severe. This rarity makes it difficult to build comprehensive datasets that include representative examples of all fault types, particularly early-stage or incipient failures [61; 62]. As a result, supervised learning models often suffer from class imbalance, overfitting, or low generalization capability outside the training distribution. Furthermore, operational data collected from power plants are often proprietary, noisy, and inconsistently formatted. Legacy systems may not store high-resolution time series or may log data at irregular intervals. Some datasets contain sensor drift, communication delays, or human-labeled events that lack consistency across facilities. Without careful preprocessing and validation, these limitations can severely impair the performance of even the most advanced algorithms. Another practical limitation lies in the integration of AI models into control and decision systems. While a model might perform well offline, embedding it into a real-time monitoring environment requires:  
* Robust interfaces with SCADA or DCS platforms;
* Response time guarantees;
* Explainability and auditability for operator trust;
* Fail-safe mechanisms to avoid false alarms or missed critical events [63; 64]. Moreover, many high-performing algorithms--such as deep neural networks--operate as black boxes, providing little insight into their internal decision logic. This opacity presents challenges in safety-critical environments, where accountability, transparency, and regulatory compliance are essential [65]. There is also a shortage of benchmark datasets and standardized evaluation protocols, making it difficult to compare methods across studies or industrial contexts. Most researchers rely on proprietary datasets, synthetic data, or limited case studies, which hinders reproducibility and generalizability. Collaborative efforts to create open, labeled datasets with detailed metadata are emerging, but adoption remains limited [66; 67]. To bridge these gaps, future diagnostic frameworks must incorporate hybrid models, capable of leveraging both physical system knowledge and data-driven learning. They must also embrace domain adaptation, unsupervised learning, and self-supervised techniques to reduce dependency on labeled data. Integration into real-time environments will require modular architectures, compliance with industrial communication standards, and user-centered design.