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微型机器学习实现以引导波为基础的损害定位

Jannik Henkmann¹, Vittorio Memmolo², Jochen Moll³

¹AG Terahertz-Photonik Physikalisches Institut, Johann Wolfgang Goethe-Universität, Max-von-Laue-Strasse 1, 60438 Frankfurt am Main, Germany.

Sensors (Basel, Switzerland)

|January 25, 2025

概括

这项研究使用超声波引导波 (UGW) 和轻量级人工智能 (AI) 来检测结构损伤. 机器学习模型以超过90%的准确度准确地确定损坏位置,从而实现高效的监控.

科学领域:

结构健康监测结构健康监测
非破坏性测试不破坏性测试
在工程领域的人工智能.

背景情况:

超声波导向波 (UGW) 对于检测结构损坏是非常有效的.
当前的损坏检测方法往往需要复杂的计算资源.
轻量级的人工智能 (AI) 提供了有效的现场损害评估的潜力.

研究的目的:

开发和验证一种基于人工智能的技术,用于使用UGW检测和定位损害.
调查机器学习 (ML) 的使用,以分析由损坏引起的UGW信号变化.
实施一个计算效率高的AI模型,用于实时结构健康监测.

主要方法:

将物理信号处理方法应用于原始UGW数据,以减少模型参数.
训练有素的机器学习模型对损坏对UGW信号的影响.
在实验基准数据集上开发和验证基于人工智能的损害检测技术.
在一个低成本的开发板上实现了一个微小的ML模型.

主要成果:

通过提取简单的信号特征,通过提取简单的信号特征,达到超过90%的高损害定位准确度.
显著减少了用于损害预测所需的AI模型的大小.
成功生成准确的热图,显示可能的损坏位置.
证明了减少计算资源和高模型精度之间的平衡.

结论:

轻量级的人工智能模型使用UGW有效地检测和定位结构损伤.
信号特征提取对于创建高效准确的损坏检测系统至关重要.
开发的技术适合在低成本硬件上实现边缘/云可视化.

关键词:

边缘人工智能边缘人工智能机器学习是机器学习.结构健康监测结构健康监测微小的设备微小的设备.超声波导向波是一种超声波导向波.

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