用大模型让风机故障诊断能说人话，还带维修建议。
LLM-YOLOMS: Large Language Model-based Semantic Interpretation and Fault Diagnosis for Wind Turbine Components
- 用多尺度检测+轻量映射，把图像结果转成带属性的文本。
- 故障检测准确率90.6%，维修报告生成准确率达89%。
- 适合风电运维人员快速理解故障并决策，提升可解释性。
风力发电机部件的健康状态对稳定运行至关重要。现有故障检测方法多依赖视觉识别，输出结构化但缺乏语义解释能力，难以支持运维决策。为此，本文提出融合YOLOMS与大语言模型（LLM）的智能故障分析与诊断框架。YOLOMS采用多尺度检测和滑动窗口裁剪增强故障特征提取，轻量级键值（KV）映射模块将视觉输出转化为包含定性与定量属性的结构化文本表示。领域微调的LLM据此进行语义推理，生成可解释的故障分析与维护建议。在真实数据集上的实验表明，该框架故障检测准确率达90.6%，维修报告生成平均准确率为89%，显著提升了诊断结果的可解释性，并为风电运维提供实用决策支持。
原文摘要 · Abstract (English)
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.
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