Search research articles

Related Concept Videos

Discrete Fourier Transform

Discrete Fourier Transform

The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...

You might also read

Related Articles

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

Sort by

Same author

Surface Crack Detection in Precasted Slab Track in High-Speed Rail via Infrared Thermography.

Materials (Basel, Switzerland)·2020

Same author

Vibration Mitigation Effect Investigation of a New Slab Track Plate Design.

Sensors (Basel, Switzerland)·2019

Same author

Degradation of the In-plane Shear Modulus of Structural BFRP Laminates Due to High Temperature.

Sensors (Basel, Switzerland)·2018

Same author

Dynamic Model Updating for Bridge Structures Using the Kriging Model and PSO Algorithm Ensemble with Higher Vibration Modes.

Sensors (Basel, Switzerland)·2018

Same author

Output-Based Structural Damage Detection by Using Correlation Analysis Together with Transmissibility.

Materials (Basel, Switzerland)·2017

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: Jan 6, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

Mono-Component Feature Extraction for Condition Assessment in Civil Structures Using Empirical Wavelet Transform.

Yun-Xia Xia¹, Yun-Lai Zhou²

¹School of Civil Engineering, Qingdao University of Technology, Qingdao 266033, China.

Sensors (Basel, Switzerland)

|October 5, 2019

Summary

This study introduces a novel scale-space empirical wavelet transform (EWT) method for analyzing structural health monitoring (SHM) signals. It accurately separates complex signal components and reliably extracts key structural features for improved infrastructure assessment.

Keywords:

civil structures empirical wavelet transform feature extraction signal processing structural health monitoring

More Related Videos

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Published on: July 19, 2016

Related Experiment Videos

Last Updated: Jan 6, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Published on: July 19, 2016

Area of Science:

Structural Engineering
Signal Processing
Data Analysis

Background:

Civil structures require continuous health monitoring to ensure safety and longevity.
Analyzing complex signals from Structural Health Monitoring (SHM) presents significant challenges.
Existing methods may struggle with closely spaced frequency components in SHM data.

Purpose of the Study:

To propose a novel methodology for processing and interpreting complex SHM signals.
To enhance the accuracy of identifying instantaneous modal parameters and structural linearity characteristics.
To validate the proposed method using both simulated and real-world SHM data.

Main Methods:

Utilizing scale-space empirical wavelet transform (EWT) for signal component separation.
Employing the FREEVIB method for instantaneous modal parameters identification.
Applying Otsu's algorithm for threshold determination in clustering meaningful modes.
Analyzing EWT-extracted mono-components to retain time-varying structural features.

Main Results:

The proposed scale-space EWT accurately separates complex and closely spaced signal components.
Reliable extraction of instantaneous modal parameters and structural linearity characteristics was achieved.
Validation with simulated and real SHM data confirmed the method's effectiveness.
The methodology demonstrated high accuracy in signal processing and feature extraction.

Conclusions:

The developed scale-space EWT methodology offers a robust approach for SHM signal analysis.
This method enhances the capability to accurately assess the health and performance of civil structures.
The findings support improved decision-making in structural maintenance and safety management.