🤖 AI Summary
This study addresses the challenge of analyzing unstructured maintenance logs from wind turbines, which hinder quantitative reliability assessment. The authors propose a model-agnostic large language model (LLM) framework that enables fully automated semantic parsing and structuring of wind turbine logs for the first time. By integrating semantic extraction, domain-specific lexicon construction, and encoding correction, the method automatically derives an evidence-based taxonomy of repair actions and failure modes. Applied to 16,316 log entries, the approach successfully structures over 70% of the data, substantially correcting misclassifications, recovering missing codes, and reducing the subjectivity inherent in traditional Failure Mode and Effects Analysis (FMEA). Furthermore, it establishes a scalable pipeline for cross-turbine reliability knowledge integration and metric generation.
📝 Abstract
As wind turbine fleets age, data-driven reliability engineering is essential to optimise their operation and maintenance for service life extension and levelised cost of energy reduction. Failure event descriptions within historical maintenance logs are a source of valuable reliability intelligence. However, they typically appear as unstructured natural language entries, rendering them inaccessible for quantitative analysis. This paper presents a novel methodology leveraging a large language model (LLM) to systematically standardise and structure maintenance logs based on their free-text descriptors. Operating on a dataset of 16,316 maintenance logs from 280 turbines monitored over nine years, the developed model-agnostic framework autonomously corrected hierarchical system codes and extracted evidence-based taxonomies of maintenance actions and failure modes. The automated pipeline successfully structured over 70% of the dataset. It resolved pervasive misclassification issues, such as isolating previously unclassified pitch system faults and restoring missing system codes, and enriched the records by applying empirical taxonomies to label specific actions taken and failure modes addressed. By using system-based log batches to construct empirical dictionaries of failure modes, observable symptoms, dominant mechanisms, and candidate causes, this approach reduces the inherent subjectivity of manual failure modes and effects analysis (FMEA). Ultimately, the methodology provides a highly scalable, cost-effective blueprint for translating large sets of qualitative field observations into quantitative reliability metrics, laying the foundation for integrated root-cause analysis across the renewable energy sector, improved FMEA, and advanced predictive maintenance.