Wind Turbine Maintenance Log Labelling Framework: LLM-Driven Data Correction and Enrichment via Semantic Extraction of Reliability Intelligence
arXiv:2605.31281v1 Announce Type: new 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
The paper leverages recent advancements in large language models to address a long-standing challenge in industrial data analysis: structuring unstructured maintenance logs.
This development allows for the conversion of previously inaccessible operational data into actionable intelligence, significantly improving asset management and potentially extending the lifespan of critical infrastructure.
Maintenance log analysis, once a manual and qualitative task, can now be systematic, quantitative, and scalable through LLM-driven automation.
- · Wind energy operators
- · Predictive maintenance software providers
- · Industrial AI companies
- · Data scientists in heavy industry
- · Traditional manual data analysis services
- · Inefficient maintenance practices
Improved reliability and efficiency for aging wind turbine fleets.
Reduced operational costs and extended asset life for other critical infrastructure through similar LLM applications.
The acceleration of AI adoption for data-driven decision making across all heavy industries, leading to new specialized LLMs for various industrial verticals.
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Read at arXiv cs.CL