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在结构健康监测中使用混合PI-DEIM方法进行增强的传感器放置优化和缺陷检测

Minyoung Yun¹, Mikhael Tannous¹, Chady Ghnatios²

¹PIMM Research Laboratory, UMR 8006 CNRS-ENSAM-CNAM, Arts et Metiers Institute of Technology, 151 Boulevard de l'Hôpital, 75013 Paris, France.

Sensors (Basel, Switzerland)

|January 11, 2025

概括

本研究介绍了一种新的混合传感器方法,用于精确定位关键位置和检测结构缺陷. 结合离散实证插值 (DEIM) 和顺序重要性 (PI) 产生了最准确的缺陷检测与最小的错误.

科学领域:

结构健康监测结构健康监测
计算力学计算力学计算力学
数据科学数据科学数据科学

背景情况:

精确识别关键传感器位置对于有效的结构健康监测至关重要.
现有的传感器放置和缺陷检测方法在准确性和速度上可能有局限性.
减少订单建模和优化算法为增强这些流程提供了潜力.

研究的目的:

开发一种新的混合方法,用于识别结构部件中的最佳传感器位置.
引入使用这些已识别的传感器的先进缺陷检测方法.
在准确性和错误减少方面评估拟议的混合方法的性能.

主要方法:

使用混合方法整合离散实证取方法 (DEIM) 和顺序重要性 (PI) 来确定最佳的传感器位置.
一种半侵入性的减少订单建模技术被用于缺陷检测.
基因搜索算法与减少顺序模型一起用于快速可靠的缺陷识别.

主要成果:

拟议的混合方法成功确定了关键传感器位置.
缺陷的错误率很低,特别是在使用混合传感器时,这些传感器结合了PI和DEIM的关键传感器.
混合传感器方法在缺陷识别方面表现出优异的性能,而不是单独使用PI或DEIM方法.

结论:

开发的混合方法为优化在结构部件中的传感器放置提供了有效的策略.
对于传感器选择的DEIM和PI的组合,可以提高缺陷检测的准确性.
拟议的减少顺序建模和遗传搜索算法为结构缺陷识别提供了快速可靠的解决方案.

关键词:

离散的实证插值方法.机器学习是机器学习.最佳的传感器放置位置.随机排列特征的重要性方法

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