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Related Experiment Video

Updated: Oct 1, 2025

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

Sim-to-real localization: Environment resilient deep ensemble learning for guided wave damage localization.

Ishan D Khurjekar¹, Joel B Harley¹

¹Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida 32608, USA.

The Journal of the Acoustical Society of America

|March 2, 2022

Summary

An ensemble deep neural network improves guided ultrasonic wave localization by training on simulated data, enhancing accuracy and resilience against environmental changes like temperature variations.

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Area of Science:

Structural health monitoring
Ultrasonic wave propagation
Artificial intelligence in engineering

Background:

Guided ultrasonic wave localization systems detect structural damage using sensor arrays and propagation models.
Environmental factors like temperature and stress introduce uncertainties, degrading system performance and model accuracy.

Purpose of the Study:

To develop a robust guided wave localization system resilient to environmental variations.
To address the performance limitations of existing algorithms due to model-reality discrepancies.

Main Methods:

An ensemble deep neural network (DNN) was developed and trained exclusively on simulated data.
The DNN approach was compared against traditional delay-and-sum and matched field processing strategies.

Main Results:

The DNN approach demonstrated superior robustness against temperature variations in experimental data.
The proposed method achieved higher accuracy with a reduced number of sensors.
The system exhibited greater resilience to spatially non-homogeneous temperature changes over time.

Conclusions:

Ensemble deep neural networks trained on simulated data offer a robust solution for guided ultrasonic wave localization.
This AI-driven approach enhances structural health monitoring by overcoming environmental uncertainties and improving damage detection accuracy.