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相关概念视频

Temperature Dependent Deformation01:12

Temperature Dependent Deformation

In a nonhomogeneous rod made up of steel and brass, restrained at both ends and subjected to a temperature change, several steps are involved in calculating the stress and compressive load. Due to the problem's static indeterminacy, one end support is disconnected, allowing the rod to experience the temperature change freely. Next, an unknown force is applied at the free end, triggering deformations in the rod's steel and brass portions. These deformations are then calculated and added together...

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无监督深度学习框架用于温度补偿损害评估,使用边缘设备上的超声波引导波.

Pankhi Kashyap1, Kajal Shivgan1, Sheetal Patil1

  • 1Department of Electrical Engineering (EE), IIT Bombay, Mumbai, 400076, India.

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概括

本研究介绍了TinyML用于引导波结构健康监测 (GW-SHM) 中的轻量级机器学习模型. 这使得复合结构中准确的设备内损坏检测成为可能,克服了云计算的限制.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 人工智能的人工智能
  • 结构工程 结构工程

背景情况:

  • 深度学习模型增强了超声波引导波结构健康监测 (GW-SHM),但需要大量的计算资源.
  • 依赖于云的部署限制了当前GW-SHM系统的可扩展性,因为处理和连接需求.
  • 环境因素和损害复杂性引入数据异质性,挑战传统的SHM方法.

研究的目的:

  • 为GW-SHM开发一种轻量级的机器学习 (ML) 解决方案,可在边缘设备上部署.
  • 为了实现高效和成本效益的结构健康监测,而无需持续的云连接.
  • 为了证明TinyML在复合结构中实时损坏检测的可行性.

主要方法:

  • 利用TinyML框架为嵌入式系统创建轻量级ML模型.
  • 开发了一个无监督学习框架来检测损坏,特别针对复合材料三明治结构中的解结和分层.
  • 通过有限元模拟和实验数据在0-90°C的温度范围内验证了该方法.
  • 在Xilinx Artix-7 FPGA上实现了一个完全集成的系统,用于数据采集,控制和边缘推理.

主要成果:

  • 轻量级的ML模型实现了相当高的准确性,尽管使用有限的功能.
  • 该系统成功地检测到小尺寸的缺陷,并提高了边缘设备的灵敏度.
  • 通过边缘部署展示了有效的在线GW-SHM能力.
  • 在不同的温度 (0-90°C) 中,TinyML方法被证明是稳健的.

结论:

  • TinyML为在资源受限的边缘设备上部署GW-SHM系统提供了一个可行的替代方案.
  • 拟议的无监督学习框架可以有效地在线检测复合结构中的损害.
  • 这项研究克服了基于云计算的SHMML解决方案的可扩展性限制.
  • 综合FPGA解决方案可方便实用的实时结构健康监测.