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使用高光谱成像和机器学习进行表面冰的检测.

Steve Vanlanduit1, Arnaud De Vooght1, Thomas De Kerf1

  • 1InViLab Research Group, Department of Electromechanical Engineering, Faculty of Applied Engineering, University of Antwerp, Groenenborgerlaan 171, 2020 Antwerpen, Belgium.

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概括
此摘要是机器生成的。

超光谱成像和机器学习有效地检测和分类表面上的冰. 这项技术显示出对像风力轮机这样的关键基础设施上监测冰的前景,提高了安全性和性能.

关键词:
超光谱成像技术的使用.探测冰的探测器机器学习是机器学习.随机的森林随机的森林支持矢量机器 (SVM) 的使用.风力轮机监控风力轮机的监控

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科学领域:

  • 材料科学 材料科学 材料科学
  • 遥感 遥感 遥感 遥感
  • 人工智能的人工智能

背景情况:

  • 冰在关键基础设施上的积累,如风力轮机叶片,显著降低性能,并构成安全风险.
  • 有效监测冰的形成对于防止故障和确保运营完整性至关重要.

研究的目的:

  • 调查高光谱成像 (HSI) 与机器学习 (ML) 结合用于检测和分类表面冰的有效性.
  • 评估ML模型在不同表面涂层和冰类型中的通用性.
  • 评估光谱带选择对分类性能的影响.

主要方法:

  • 在受控的实验室条件下使用推扫 HSI 系统获取超光谱反射率数据.
  • 使用热电冷却装置在涂层和未涂层表面上产生光泽和光冰.
  • 培训和评估支持矢量机 (SVM) 和随机森林 (RF) 分类器的光谱数据.

主要成果:

  • 两种SVM和RF模型都显示了对表面冰检测的高分类准确性.
  • 由于光吸收率更高,模型性能在涂黑表面下降.
  • 通过使用多类射频方法来区分光泽和光冰.
  • 模型性能对光谱频段选择敏感,SVM在减少频段和RF在全光谱范围内是最佳的.

结论:

  • 在安全关键的应用中,HSI与ML相结合,为实时地表冰监测提供了强大的解决方案.
  • 选择ML模型和光谱范围会影响冰的分类准确性,特别是在各种表面上.
  • 这种方法为为基础设施开发有针对性的冰减缓策略提供了有价值的数据.