LLM-YOLOMS: Large Language Model-based Semantic Interpretation and Fault Diagnosis for Wind Turbine Components

TL;DR

This paper introduces an integrated framework combining YOLOMS and a large language model to enhance fault detection, interpretability, and maintenance decision support for wind turbine components, addressing limitations of visual-only methods.

Contribution

It presents a novel combination of visual detection with semantic reasoning using LLMs, enabling structured fault analysis and maintenance recommendations for wind turbines.

Findings

Fault detection accuracy of 90.6%

Maintenance report accuracy of 89%

Improved interpretability of fault diagnostics

Abstract

The health condition of wind turbine (WT) components is crucial for ensuring stable and reliable operation. However, existing fault detection methods are largely limited to visual recognition, producing structured outputs that lack semantic interpretability and fail to support maintenance decision-making. To address these limitations, this study proposes an integrated framework that combines YOLOMS with a large language model (LLM) for intelligent fault analysis and diagnosis. Specifically, YOLOMS employs multi-scale detection and sliding-window cropping to enhance fault feature extraction, while a lightweight key-value (KV) mapping module bridges the gap between visual outputs and textual inputs. This module converts YOLOMS detection results into structured textual representations enriched with both qualitative and quantitative attributes. A domain-tuned LLM then performs semantic reasoning to generate interpretable fault analyses and maintenance recommendations. Experiments on real-world datasets demonstrate that the proposed framework achieves a fault detection accuracy of 90.6\% and generates maintenance reports with an average accuracy of 89\%, thereby improving the interpretability of diagnostic results and providing practical decision support for the operation and maintenance of wind turbines.