# Exploratory Semantic Reliability Analysis of Wind Turbine Maintenance Logs using Large Language Models

arXiv: https://arxiv.org/abs/2509.22366 · HTML: https://mlsanity.com/paper/2509.22366/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 26 Sep 2025.

## Builds on (most cited first, 4 of 4)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Automatic Labelling with Open-source LLMs using Dynamic Label Schema Integration](https://mlsanity.com/paper/2501.12332/) (2025)
- [SafeLLM: Domain-Specific Safety Monitoring for Large Language Models: A Case Study of Offshore Wind Maintenance](https://mlsanity.com/paper/2410.10852/) (2024)
- [A Comparative Benchmark of Large Language Models for Labelling Wind Turbine Maintenance Logs](https://mlsanity.com/paper/2509.06813/) (2025)

## Built on by (most cited first, 1 of 1)

- [Wind Turbine Maintenance Log Labelling Framework: LLM-Driven Data Correction and Enrichment via Semantic Extraction of Reliability Intelligence](https://mlsanity.com/paper/2605.31281/) (2026)

## Abstract (arXiv)

A wealth of operational intelligence is locked within the unstructured free-text of wind turbine maintenance logs, a resource largely inaccessible to traditional quantitative reliability analysis. While machine learning has been applied to this data, existing approaches typically stop at classification, categorising text into predefined labels. This paper addresses the gap in leveraging modern large language models (LLMs) for more complex reasoning tasks. We introduce an exploratory framework that uses LLMs to move beyond classification and perform deep semantic analysis. We apply this framework to a large industrial dataset to execute four analytical workflows: failure mode identification, causal chain inference, comparative site analysis, and data quality auditing. The results demonstrate that LLMs can function as powerful "reliability co-pilots," moving beyond labelling to synthesise textual information and generate actionable, expert-level hypotheses. This work contributes a novel and reproducible methodology for using LLMs as a reasoning tool, offering a new pathway to enhance operational intelligence in the wind energy sector by unlocking insights previously obscured in unstructured data.