使用深度视觉大模型DETR + X和数字双胞胎进行变压器智能操作和维护的新方法
Xuedong Zhang1, Wenlei Sun2, Ke Chen3
1School of Intelligent Manufacturing Modern Industry, Xinjiang University, Urumqi, 830046, Xinjiang, China.
Scientific reports
|January 3, 2025
概括
一个新的深度视觉和数字双胞胎框架使实时变压器监控成为可能. 增强的DETR+X模型在变压器状态识别中实现了100%的准确性,并改善了对象检测以实现智能维护.
科学领域:
- 电气工程 电气工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 传统的变压器监控方法缺乏实时功能和智能决策.
- 将深度学习与数字双胞胎集成为先进诊断提供了一个有希望的方法.
研究的目的:
- 开发一种用于变压器实时监控和智能维护的新型框架.
- 增强视觉检测模型,以改进变压器状态识别和对象检测.
主要方法:
- 开发了一个增强的视觉检测模型,DETR+X,使用多维数据增强 (Swin2SR,GANs).
- 利用金字塔视觉变压器 (PVT) 和可变形的注意力来进行特征提取和处理多尺度特征.
- 集成了一个微调的Llava-7b模型,用于变压器维护中的专家决策.
- 构建了一个全面的平台,利用数字双胞胎和推理模型.
主要成果:
- 在变压器状态识别中,DETR+X模型实现了DGA特征图的100%分类准确性.
- 在物体检测mAP50得分方面,超越了现有的模型 (更快的R-CNN,RetinaNet,YOLOv8,可变形DETR),特别是对于小物体.
- 拉拉瓦-7b模型为变压器维护提供了准确的操作建议.
结论:
- 开发的框架有效地实现了变压器的实时监控和智能维护.
- 增强的DETR+X模型显著改善了变压器状态识别和对象检测功能.
- 深度视觉,数字双胞胎和专家人工智能模型的整合为预测性维护提供了强大的解决方案.
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