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

Vector Algebra: Method of Components01:08

Vector Algebra: Method of Components

13.8K
It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...
13.8K
Discrete Fourier Transform01:15

Discrete Fourier Transform

245
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...
245
Bearings: Problem Solving01:24

Bearings: Problem Solving

282
Understanding the calculations and concepts related to double-collar bearings is essential for engineers and designers to optimize the performance of these components in various applications. By analyzing the bearing under different conditions, one can ensure that it can withstand the forces and moments experienced during operation. This knowledge enables better decision-making when designing and selecting bearings for specific purposes and configurations. Consider a double-collar bearing with...
282
Load along a Single Axis01:29

Load along a Single Axis

294
In structural engineering, the analysis of beams subjected to varying loads is a critical aspect of understanding the behavior and performance of these structural elements. A common scenario involves a beam subjected to a combination of different load distributions.
Consider a beam of length L subjected to a varying load, which is a combination of parabolic and trapezoidal load distribution along the x-axis. In this case, it is essential to determine the resultant loads, their locations, and...
294
Eccentric Axial Loading in a Plane of Symmetry01:16

Eccentric Axial Loading in a Plane of Symmetry

176
Eccentric axial loading occurs when an axial load is applied away from the centroidal axis of a structural member. This scenario is common in engineering, where structural elements may not be directly aligned due to various design or functional requirements.
176
IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

960
Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
960

You might also read

Related Articles

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

Sort by
Same author

Traffic Light Recognition Assistant for Color Vision Deficiency Using YOLO with Multilingual Audio Feedback.

Sensors (Basel, Switzerland)·2026
Same author

Fault detection of high-speed train wheelset bearings based on improved auxiliary classifier generative adversarial networks and VAE.

PloS one·2025
Same author

Optimization of shunting operation plan in large freight train depot based on DQN algorithm.

PloS one·2025
Same author

Classification of offshore wind grid-connected power quality disturbances based on fast S-transform and CPO-optimized convolutional neural network.

PloS one·2024
Same author

A Foggy Weather Simulation Algorithm for Traffic Image Synthesis Based on Monocular Depth Estimation.

Sensors (Basel, Switzerland)·2024

Related Experiment Video

Updated: Jun 18, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

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

Published on: January 5, 2024

1.0K

Research on a Fault Feature Extraction Method for an Electric Multiple Unit Axle-Box Bearing Based on a

Jiandong Qiu1, Qiang Zhang1, Minan Tang2

  • 1School of Mechanical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.

Sensors (Basel, Switzerland)
|July 27, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an optimized signal decomposition method to accurately detect bearing faults in electric multiple units (EMUs) despite noisy operational data. The approach enhances fault characteristic frequency extraction for reliable diagnostics.

Keywords:
axle-box bearing of EMUfault feature extractionresonance-based sparse signal decompositionvariational mode decomposition

More Related Videos

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

1.6K
Characterizing the Composition of Molecular Motors on Moving Axonal Cargo Using "Cargo Mapping" Analysis
11:09

Characterizing the Composition of Molecular Motors on Moving Axonal Cargo Using "Cargo Mapping" Analysis

Published on: October 30, 2014

9.3K

Related Experiment Videos

Last Updated: Jun 18, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

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

Published on: January 5, 2024

1.0K
Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

1.6K
Characterizing the Composition of Molecular Motors on Moving Axonal Cargo Using "Cargo Mapping" Analysis
11:09

Characterizing the Composition of Molecular Motors on Moving Axonal Cargo Using "Cargo Mapping" Analysis

Published on: October 30, 2014

9.3K

Area of Science:

  • Mechanical Engineering
  • Signal Processing
  • Condition Monitoring

Background:

  • Electric multiple units (EMUs) rely on axle-box bearings for operational integrity.
  • Background noise in vibration signals complicates the identification of bearing fault characteristic frequencies.
  • Accurate fault detection is crucial for preventing catastrophic failures and ensuring safety.

Purpose of the Study:

  • To propose a novel method for extracting bearing fault characteristic frequencies from noisy vibration signals in EMUs.
  • To enhance the accuracy and robustness of fault diagnosis in axle-box bearings.
  • To reduce reliance on subjective parameter settings in signal processing.

Main Methods:

  • Utilized resonance-based sparse signal decomposition (RSSD) to isolate low-resonance components containing fault information.
  • Applied variational mode decomposition (VMD) to refine the extracted fault signals.
  • Optimized parameters for both RSSD and VMD using the sparrow search algorithm (SSA).
  • Employed envelope demodulation on the kurtosis-maximizing intrinsic mode function (IMF) for final diagnosis.

Main Results:

  • The proposed RSSD-VMD method effectively extracted more distinct periodic fault impact components.
  • Demonstrated significant filtering of complex background noise and interference.
  • Experimental validation using simulated and real-world signals confirmed the method's efficacy.
  • Achieved superior fault characteristic frequency extraction compared to traditional methods.

Conclusions:

  • The SSA-optimized RSSD-VMD method provides a robust and adaptable solution for bearing fault diagnosis in noisy EMU environments.
  • This technique improves the reliability of fault detection by minimizing human experience-dependent parameter tuning.
  • The findings contribute to advanced condition monitoring strategies for railway systems.