Search research articles

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Fatigue Crack Length Estimation Using Acoustic Emissions Technique-Based Convolutional Neural Networks.

Sensors (Basel, Switzerland)·2026

Same author

Exploring Piezoelectric Actuation towards Its Applications in Laser Powder Bed Fusion Additive Manufacturing.

Sensors (Basel, Switzerland)·2024

Same author

Piezoelectric Transducer-Based Structural Health Monitoring.

Sensors (Basel, Switzerland)·2024

Same author

Delamination Depth Detection in Composite Plates Using the Lamb Wave Technique Based on Convolutional Neural Networks.

Sensors (Basel, Switzerland)·2024

Same author

Piezoelectric Wafer Active Sensor Transducers for Acoustic Emission Applications.

Sensors (Basel, Switzerland)·2023

Same author

Crack-Length Estimation for Structural Health Monitoring Using the High-Frequency Resonances Excited by the Energy Release during Fatigue-Crack Growth.

Sensors (Basel, Switzerland)·2021

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: May 10, 2025

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Published on: June 16, 2014

New Method of Impact Localization on Plate-like Structures Using Deep Learning and Wavelet Transform.

Asaad Migot^1,2, Ahmed Saaudi^2,3, Victor Giurgiutiu²

¹Department of Petroleum and Gas Engineering, College of Engineering, University of Thi-Qar, Nasiriyah 64001, Iraq.

Sensors (Basel, Switzerland)

|April 28, 2025

Summary

This study introduces a new method using a two-dimensional convolutional neural network (CNN) and piezoelectric wafer active sensors (PWAS) to precisely locate impacts on structures. The model achieved high accuracy in impact localization, demonstrating its effectiveness for structural health monitoring.

Keywords:

PWAS convolutional neural network (CNN)deep learning (DL)impact localization wavelet transform (WT)

More Related Videos

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Published on: July 3, 2021

Control of Cell Adhesion using Hydrogel Patterning Techniques for Applications in Traction Force Microscopy

Control of Cell Adhesion using Hydrogel Patterning Techniques for Applications in Traction Force Microscopy

Published on: January 29, 2022

Related Experiment Videos

Last Updated: May 10, 2025

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Published on: June 16, 2014

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Published on: July 3, 2021

Control of Cell Adhesion using Hydrogel Patterning Techniques for Applications in Traction Force Microscopy

Control of Cell Adhesion using Hydrogel Patterning Techniques for Applications in Traction Force Microscopy

Published on: January 29, 2022

Area of Science:

Structural Health Monitoring
Signal Processing
Machine Learning Applications

Background:

Impact events on plate-like structures generate reflection waves detectable by sensors.
Accurate localization of impacts is crucial for assessing structural integrity and damage.
Existing methods may face challenges in differentiating similar signal features.

Purpose of the Study:

To develop and evaluate a novel methodology for localizing impact events on plate-like structures.
To utilize a two-dimensional convolutional neural network (CNN) for processing impact signals.
To assess the performance of the proposed CNN model in impact localization accuracy.

Main Methods:

A network of piezoelectric wafer active sensors (PWAS) was used to acquire impact signals.
Received signals underwent wavelet transform (WT)-based time-frequency analysis.
Processed WT diagrams were used as image datasets to train and test a 2D CNN model.
Two sensor placement scenarios were investigated with varying numbers of impacts.

Main Results:

The proposed CNN model demonstrated exceptional performance in impact localization.
All impact points were accurately localized in the first scenario (two sensors).
The model achieved 99% accuracy in localizing impacts in the second scenario (four sensors).

Conclusions:

The developed CNN-based methodology is highly effective for localizing impact events on plate-like structures.
The study highlights the potential of WT-based signal processing and CNNs for structural health monitoring.
Future work may focus on addressing challenges related to differentiating similar signal features arising from segmentation and impact proximity.