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一个嵌入物理的深度学习框架,用于高效的多忠度建模,应用于基于引导波的结构健康监测

Vivek Nerlikar¹, Roberto Miorelli¹, Arnaud Recoquillay¹

¹Université Paris-Saclay, CEA, List, F-91120, Palaiseau, France.

|May 3, 2024

概括

这项研究引入了一个深度学习的数字双胞胎来改善结构性健康监测. 它减少了模拟差异,使机器学习算法能够进行更可靠的评估和数据增强.

科学领域:

工程工程师工程师工程师
材料科学材料科学材料科学
人工智能的人工智能

背景情况:

超声波导向波用于结构健康监测,但对外部因素敏感,导致模拟和现实世界测量之间的差异.
准确地建模这些影响因素是具有挑战性的,影响基于引导波的监控系统的可靠性.
解决模拟测量差异对于生成现实的引导波响应至关重要.

研究的目的:

开发基于深度学习的数字双胞胎框架,以减少结构性健康监测中的数值模拟和实验测量之间的差异.
通过使用多忠实度建模和深度生成模型,生成逼真的,接近实验的引导波响应.
为了能够评估监控系统的可靠性和增强机器学习算法的训练数据.

主要方法:

基于深度学习的数字双胞胎框架的实施,包括多忠实度建模.
使用深度生成模型来合成模拟实验数据的引导波响应.
使用裂传播和相应模拟的测量数据集进行验证.

主要成果:

显著减少模拟和实验测量之间的差异.
成功生成现实的,接近实验的引导波响应.
证明框架能够从生成的数据中计算检测概率.

结论:

拟议的数字双胞胎框架有效地减少了超声波引导波监测中的模拟测量差异.
该框架有助于生成现实的模拟,以提高可靠性评估和数据增强.
这种方法通过提高模拟准确性,促进了在结构健康监测中使用引导波的应用.

关键词:

深度生成模型深度生成模型深度学习多忠度建模深度学习多忠度建模数字双胞胎数字双胞胎数字双胞胎物理嵌入式机器学习模拟到实验的模拟结构性健康监测结构性健康监测超声波导向波是一种超声波导向波.

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