Analyzing Power Plant Data Using Artificial Intelligence to Enhance Maintenance Strategy

Abstract

This research aims to optimize maintenance strategies in power plants by leveraging artificial intelligence (AI) techniques to analyze historical operational data. The study adopts a quantitative analytical approach, utilizing deep learning algorithms—including GRU, LSTM, and TCN—to detect anomalies and predict equipment malfunctions. Historical data from a power plant, encompassing sensor readings, fault logs, and operational parameters, were collected, pre-processed, and analyzed. The results demonstrate that the GRU algorithm outperforms other models, achieving an accuracy exceeding 83% and the lowest loss value, thereby proving its robustness in generalization and predictive capability. The proposed system significantly reduces unplanned downtime, minimizes maintenance costs, and enhances operational efficiency. A practical case study confirms the effectiveness of the approach in real-world settings. The integration of AI into power plant maintenance not only improves system reliability but also supports sustainability objectives, establishing AI-driven predictive maintenance as a strategic asset for the modern energy sector.