LLMs in wind turbine gearbox failure prediction

Tools

Tan, Yoke Wang and Carroll, James (2025) LLMs in wind turbine gearbox failure prediction. Energies, 18 (17). 4659. ISSN 1996-1073 (https://doi.org/10.3390/en18174659)

[thumbnail of Tan-etal-Energies-2025-LLMs-in-wind-turbine-gearbox-failure-prediction]
Preview
 Text. Filename: Tan-etal-Energies-2025-LLMs-in-wind-turbine-gearbox-failure-prediction.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (1MB)| Preview

Abstract

Predictive maintenance strategies in wind turbine operations have risen in popularity with the growth of renewable electricity demand. The capacity of the strategy to predict system health, especially for the wind turbine gearboxes, is critical in reducing wind turbine operation and maintenance cost. Driven by the emergence of the application of large language models (LLMs) in diverse domains, this work explores the potential of LLMs in the development of wind turbine gearbox prognosis. A comparative analysis is designed to investigate the capability of two state-of-the-art LLMs—GPT-4o and DeepSeek-V3—in proposing machine learning (ML) pipelines to classify gearbox conditions based on a labelled SCADA dataset. The LLMs were prompted with the context of the task and detailed information about the SCADA dataset investigated. The outputs generated by the LLMs were evaluated in terms of pipeline quality and prediction performance using the confusion metric. Baseline ML models were developed and fine-tuned as benchmarks using Python 3.12 libraries. Among the baseline models, the random forest and XGBoost models achieved the highest cross-validated average F1-scores. The results have shown that the ML pipeline proposed by DeepSeek-V3 was significantly better than both GPT-4o and baseline models in terms of data analytical scope and prediction accuracy.

ORCID iDs

Tan, Yoke Wang and Carroll, James ORCID logoORCID: https://orcid.org/0000-0002-1510-1416;
CORE (COnnecting REpositories)