Enhancing Maintenance Decision-Making in Thermal Power Plants Using Generative AI-Based Fault Diagnosis - Shreekantrao; Nagendra Kumar Swarnkar (International Journal of Energy and Thermal Applications, 2026)
The growing complexity of operation and power consumption of thermal power stations involve the need to have intelligent fault diagnosis systems that can be used to guarantee reliability and safety in operation. In this research, a Generative AI (GenAI)-based hybrid architecture of early fault detection and predictive maintenance is proposed to improve the decision-making process of the maintenance team. The data-driven analytic approach combines methods of data-driven analytics, Generative AI models, and supervised learning algorithms including Long Short-Term Memory (LSTM), Random Forest (RF), and XGBoost. The sensor data of plant components such as voltage, current, temperature and vibration are first pre-processed and feature engineered to come up with important fault indicators. Exploratory Data Analysis (EDA) indicates that there are correlations between the operational stress and fault occurrence. The Generative AI module enhances the dataset by generating rare fault samples, which solve the issue of data imbalance and enhance the learning robustness. Models that were trained on this better dataset exhibit a high level of accuracy, precision and reliability of fault prediction. Experimental findings indicate that, the GenAI-augmented Random Forest model had the best performance of 97.2% accuracy and 96% F1-score, which was better than the traditional approaches. The created framework allows real-time forecast of faults, active maintenance planning, and significant minimization of unplanned downtime. The study will help to develop intelligent and data-driven maintenance systems to reach Industry 4.0 goals of digitalizing power stations and making them sustainable.
Keywords: Fault diagnosis; Predictive Maintenance; Thermal Power Plant; generative artificial intelligence; machine learning models