Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Strategies of Self-Presentation III: Self-Monitoring01:24

Strategies of Self-Presentation III: Self-Monitoring

265
Self-monitoring is a central construct in understanding individual differences in self-presentation strategies across social contexts. It refers to how individuals observe, regulate, and control their expressive behavior and self-presentation following situational cues. Self-monitoring reflects a person's sensitivity to social appropriateness and willingness to adapt behavior to fit varying interpersonal demands.High vs. Low Self-Monitoring IndividualsIndividuals high in self-monitoring are...
265
Convolution Properties II01:17

Convolution Properties II

588
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
588
Protein Networks02:26

Protein Networks

4.6K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.6K
Convolution Properties I01:20

Convolution Properties I

609
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
609
Kidney Structure01:45

Kidney Structure

75.4K
The kidneys are two large bean-shaped organs located in the upper abdomen. They filter the blood several times a day to remove toxins and rebalance water and electrolytes of the circulatory system via the renal veins. The kidneys receive blood directly from the heart via the renal arteries. These arteries enter the kidney at the hilum, the concave surface of the bean, where they branch and divide into smaller vessels and capillaries.
75.4K
Network Covalent Solids02:18

Network Covalent Solids

16.2K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.2K

You might also read

Related Articles

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

Sort by
Same author

Domain-Adaptive Graph Attention Semi-Supervised Network for Temperature-Resilient SHM of Composite Plates.

Sensors (Basel, Switzerland)·2025
Same author

Structural Damage Detection Using PZT Transmission Line Circuit Model.

Sensors (Basel, Switzerland)·2024
Same author

Osteoporosis Screening: Applied Methods and Technological Trends.

Medical engineering & physics·2022
Same author

Forensic Speaker Verification Using Ordinary Least Squares.

Sensors (Basel, Switzerland)·2019
Same author

Modeling, Simulation, Experimentation, and Compensation of Temperature Effect in Impedance-Based SHM Systems Applied to Steel Pipes.

Sensors (Basel, Switzerland)·2019
Same author

Geometrical features for premature ventricular contraction recognition with analytic hierarchy process based machine learning algorithms selection.

Computer methods and programs in biomedicine·2019

Related Experiment Video

Updated: Feb 5, 2026

Investigating the Potential of Singly Curved Thin Piezoelectric Transducers for Energy Harvesting and Structural Health Monitoring
07:02

Investigating the Potential of Singly Curved Thin Piezoelectric Transducers for Energy Harvesting and Structural Health Monitoring

Published on: November 14, 2025

861

A New Structural Health Monitoring Strategy Based on PZT Sensors and Convolutional Neural Network.

Mario A de Oliveira1, Andre V Monteiro2, Jozue Vieira Filho3

  • 1Department of Electrical and Electronic, Mato Grosso Federal Institute of Technology, Cuiabá 78005-200, Brazil. mario.oliveira@cba.ifmt.edu.br.

Sensors (Basel, Switzerland)
|September 8, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a novel structural health monitoring (SHM) method combining electromechanical impedance (EMI) with lead zirconate titanate (PZT) sensors and convolutional neural networks (CNNs). The innovative approach achieves 100% accuracy in classifying structural conditions, outperforming existing SHM techniques.

Keywords:
CNNSHMdeep learningelectromechanical impedanceintelligent fault diagnosismachine learningpiezoelectricity

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

10.0K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.1K

Related Experiment Videos

Last Updated: Feb 5, 2026

Investigating the Potential of Singly Curved Thin Piezoelectric Transducers for Energy Harvesting and Structural Health Monitoring
07:02

Investigating the Potential of Singly Curved Thin Piezoelectric Transducers for Energy Harvesting and Structural Health Monitoring

Published on: November 14, 2025

861
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

10.0K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.1K

Area of Science:

  • Structural Health Monitoring (SHM)
  • Artificial Intelligence in Engineering
  • Materials Science

Background:

  • Convolutional Neural Networks (CNNs) are increasingly used in SHM, primarily for vibration analysis.
  • Existing SHM research shows limited application of CNNs combined with lead zirconate titanate (PZT) based methods or the electromechanical impedance (EMI) technique.
  • There is a need for innovative SHM solutions that integrate advanced sensing with machine learning.

Purpose of the Study:

  • To present an innovative SHM solution by combining the EMI-PZT method with CNNs.
  • To address the scarcity of research on CNN applications within the EMI-PZT framework for SHM.
  • To develop a robust pattern classification method for assessing structural conditions.

Main Methods:

  • The electromechanical impedance (EMI) signature is segmented into multiple parts.
  • Euclidean distances are computed between segments to generate RGB frames.
  • A dataset of 720 EMI-PZT signal frames representing four structural conditions was created.
  • A CNN model was trained and experimentally evaluated on an aluminum plate with three PZT sensors.

Main Results:

  • The CNN-based SHM method demonstrated effective pattern classification capabilities.
  • An exceptional 100% hit rate was achieved in classifying structural conditions.
  • The proposed method requires only a small dataset for CNN training, enhancing its practicality.
  • Performance significantly outperformed other existing SHM approaches.

Conclusions:

  • The integration of EMI-PZT sensing with CNNs offers a highly effective and accurate SHM solution.
  • This method shows great promise for industrial applications due to its high accuracy and minimal data requirements.
  • The study successfully validates the potential of this combined approach for advanced structural monitoring.