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Ultrasonography01:17

Ultrasonography

Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
During an ultrasonography procedure, a handheld device called a...
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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Mitigating the Impact of Temperature Variations on Ultrasonic Guided Wave-Based Structural Health Monitoring through

Rafael Junges1, Luca Lomazzi1, Lorenzo Miele1

  • 1Politecnico di Milano, Department of Mechanical Engineering, Via La Masa n.1, 20156 Milan, Italy.

Sensors (Basel, Switzerland)
|March 13, 2024
PubMed
Summary

This study introduces a novel generative artificial intelligence approach to reduce environmental impacts on structural health monitoring (SHM) using guided waves. The method effectively mitigates temperature variations, enhancing damage detection accuracy in plated structures.

Keywords:
generative artificial intelligencetemperatureultrasonic guided wavevariational autoencoder

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Area of Science:

  • Materials Science and Engineering
  • Artificial Intelligence and Machine Learning
  • Non-Destructive Testing and Evaluation

Background:

  • Structural health monitoring (SHM) is crucial for cost-effective maintenance of plated structures.
  • Current SHM methods use ultrasonic guided waves and sensor networks for damage detection.
  • Existing data-driven approaches improve diagnostic performance but struggle with environmental and operational conditions (EOCs).

Purpose of the Study:

  • To demonstrate the efficacy of machine learning in reducing EOC impacts on SHM.
  • To specifically address and mitigate temperature variations affecting ultrasonic guided wave signals.
  • To enhance the reliability and accuracy of damage diagnosis in SHM systems.

Main Methods:

  • Leveraged generative artificial intelligence, specifically variational autoencoders (VAEs) and singular value decomposition (SVD).
  • Trained the model to learn the influence of temperature on guided wave propagation.
  • Utilized the generative capabilities of the trained model to reconstruct signals under varying temperatures, including a refined 'forced VAE' approach.

Main Results:

  • Successfully demonstrated the capability of generative AI to mitigate temperature effects on guided wave signals.
  • The proposed framework showed improved signal reconstruction accuracy at unseen temperatures.
  • Validation against real-world measurements on a composite plate confirmed the framework's effectiveness.

Conclusions:

  • Generative AI, particularly VAEs combined with SVD, offers a powerful solution for handling EOCs in SHM.
  • The developed method significantly reduces the impact of temperature variations, improving damage diagnosis reliability.
  • This proof-of-concept paves the way for more robust and accurate SHM systems in challenging operational environments.