Optimization of Large Language Models with Multimodal Data Training for Fault Diagnosis in Wind Turbine Systems

Wind turbines are critical components of renewable energy systems, but their operational efficiency and reliability are often compromised by faults that lead to costly maintenance and downtime. This study presents an innovative multimodal fault diagnosis system for wind turbines, integrating advanced data fusion techniques with large language models (LLMs) to enhance diagnostic accuracy, interpretability, and real-time performance. By combining vibration, acoustic, thermal, and visual data through a hybrid CNN-LLM architecture, the system achieves a 91.5 % overall fault classification accuracy, outperforming traditional single-modality approaches by 6.7 %. The integration of LLMs enables the generation of human-readable diagnostic reports with an average BLEU score of 0.84, significantly improving technician understanding and decision-making. Field deployment over three months demonstrated the system's practical impact, reducing maintenance costs by 23.4 % and emergency repairs by 56 %, while achieving a mean fault detection latency of 8.7 minutes. Despite its success, limitations such as weather sensitivity and computational latency highlight opportunities for future research, including edge deployment optimization, robustness enhancement, and extension to other renewable energy systems. This research advances the field of predictive maintenance, offering a scalable and efficient solution for improving wind turbine reliability and operational efficiency.