🤖 AI Summary
To address the lack of semantic interpretability in visual inspection results for wind turbine (WT) component fault diagnosis—hindering operational and maintenance decision-making—this paper proposes a vision–language collaborative diagnostic framework. First, YOLOMS with multi-scale detection and sliding-window cropping enhances small-object recognition accuracy. Second, a lightweight key-value (KV) mapping module automatically converts structured detection outputs—including bounding box coordinates, class labels, and confidence scores—into qualitative and quantitative natural-language descriptions. Third, a domain-adapted large language model (LLM) performs semantic reasoning to generate human-understandable fault analyses and actionable maintenance recommendations. Evaluated on a real-world dataset, the framework achieves 90.6% fault detection accuracy and 89% accuracy in generating maintenance reports, significantly improving both the interpretability and engineering applicability of diagnostic outcomes.
📝 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.