Wind Turbine Maintenance Log Labelling Framework: LLM-Driven Data Correction and Enrichment via Semantic Extraction of Reliability Intelligence
Researchers have developed a framework using large language models (LLMs) to standardize and structure unstructured maintenance logs from wind turbines. This methodology processes historical data to extract reliability intelligence, correcting system codes and identifying maintenance actions and failure modes. The automated pipeline successfully structured over 70% of a dataset comprising 16,316 logs from 280 turbines, aiming to improve failure analysis and predictive maintenance in the renewable energy sector. AI
IMPACT Enables more accurate predictive maintenance and root-cause analysis in the renewable energy sector by structuring unstructured data.