Wind Turbine Maintenance Log Labelling Framework: LLM-Driven Data Correction and Enrichment via Semantic Extraction of Reliability Intelligence

원문 초록

Inconsistent codes and unstructured text limit the use of wind turbine maintenance records in reliability analysis. A topology-aware large language model (LLM) workflow corrected and enriched 16,316 records from 280 turbines at 31 onshore wind farms over 9.2 years. Maintenance narratives and equipment context guided system-code correction and maintenance-type relabelling. System- and topology-specific batches yielded candidate action and failure-mode taxonomies for record-level labelling. Failure-mode profiles linked labels to common mechanisms, possible symptoms and candidate causes. Schema constraints, deterministic rules and source links supported auditable processing. Proposed labels required model-reported High confidence and no human-review flag. Accepted system-code updates covered 2,178 of 2,984 targeted records; accepted failure-mode labels covered 11,661 records (71.5%). The enriched fields separated pitch interventions from broader system categories and distinguished electrical and hydraulic pitch maintenance patterns. Recorded task durations totalled 6.83 hours. A source-based assessment covered 800 records selected by balanced probability sampling. Changed system codes and maintenance types required source support; retained specific labels were approved unless contradicted. Equal-weight mean approval across four fields was 86.3%: 94.8% for system codes, 87.3% for maintenance types, 85.6% for actions and 77.5% for failure-mode outcomes. Failure-mode assessment covered titles and unresolved, unmatched or inapplicable statuses. Qualitative reviews identified unsupported actions and unclear category boundaries. The structured records provide traceable evidence for engineering review and future operational linkage supporting event reconstruction, exposure-based reliability analysis and failure modes and effects analysis.