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

Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

428
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
428
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

492
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
492
Multimachine Stability01:25

Multimachine Stability

207
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
207
Relative Motion Analysis using Rotating Axes - Acceleration01:22

Relative Motion Analysis using Rotating Axes - Acceleration

361
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame. The absolute velocity of point B is determined by adding the absolute velocity of point A, the relative velocity of point B in the rotating frame, and the effects caused by the angular velocity within the rotating frame.
Time differentiation is...
361
Transmission Shafts: Problem Solving01:09

Transmission Shafts: Problem Solving

267
Designing a solid shaft that transmits power from a motor to a machine tool involves a series of calculations to ensure the shaft can withstand the stresses applied by bending moments and torques. First, calculate the torque exerted on the gear, considering the power transmitted by the shaft and its rotational speed. Following this, compute the tangential forces acting on the gears, which directly relate to the torque and the gear radius.
Next, use bending moment diagrams for the shaft to...
267
Relative Motion Analysis - Velocity01:24

Relative Motion Analysis - Velocity

397
A stroke engine has a slider-crank mechanism that converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider.
When an external force is exerted, it sets the crank into a rotational movement. This, in turn, instigates the motion of the connecting rod, leading to what is referred to as a general plane motion. This process involves two key points - point A on the connecting rod...
397

You might also read

Related Articles

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

Sort by
Same author

Speckle Noise Removal Model Based on Diffusion Equation and Convolutional Neural Network.

Computational intelligence and neuroscience·2022
Same author

First-principles theory of quantum well resonance in double barrier magnetic tunnel junctions.

Physical review letters·2006
Same author

Common non-synonymous polymorphisms in the BRCA1 Associated RING Domain (BARD1) gene are associated with breast cancer susceptibility: a case-control analysis.

Breast cancer research and treatment·2006
Same author

[Identification of two novel mutations of human blood coagulation factor V gene in a Chinese family with congenital factor V deficiency].

Zhonghua yi xue yi chuan xue za zhi = Zhonghua yixue yichuanxue zazhi = Chinese journal of medical genetics·2006
Same author

Identification of glycoproteins in human cerebrospinal fluid with a complementary proteomic approach.

Journal of proteome research·2006
Same author

Kaposi's sarcoma-associated herpesvirus ori-Lyt-dependent DNA replication: dual role of replication and transcription activator.

Journal of virology·2006

Related Experiment Video

Updated: Jul 30, 2025

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

1.7K

Fault detection of gearbox by multivariate extended variational mode decomposition-based time-frequency images and

Siwei Nao1, Yan Wang2

  • 1Medical Technology School, Qiqihar Medical University, Qiqihar, 161000, China.

Scientific Reports
|May 16, 2023
PubMed
Summary

A new method using multivariate extended variational mode decomposition and an incremental RVM algorithm improves gearbox fault detection. This approach offers stable and superior performance compared to existing techniques for complex signals.

More Related Videos

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

1.2K
A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

3.1K

Related Experiment Videos

Last Updated: Jul 30, 2025

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

1.7K
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

1.2K
A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

3.1K

Area of Science:

  • Mechanical Engineering
  • Signal Processing
  • Machine Learning

Background:

  • Gearbox fault detection is crucial for industrial machinery maintenance.
  • Traditional methods struggle with non-stationary, low signal-to-noise ratio data.
  • Existing signal processing techniques lack robustness for multi-channel gearbox signals.

Purpose of the Study:

  • To introduce a novel fault detection method for gearboxes.
  • To enhance the accuracy and robustness of gearbox fault diagnosis.
  • To compare the proposed method against established algorithms.

Main Methods:

  • Utilizing multivariate extended variational mode decomposition (MEVMDTFI) for time-frequency image construction.
  • Employing an incremental relevance vector machine (IRVM) algorithm for fault classification.
  • Developing a combined MEVMDTFI-IRVM approach for gearbox fault detection.

Main Results:

  • The MEVMDTFI-IRVM method demonstrated stable fault detection results for gearboxes.
  • The proposed method significantly outperformed VMDTFI-IRVM, VMD-RVM, and traditional RVM algorithms.
  • MEVMDTFI showed superior robustness for non-stationary, multi-channel signals with low SNR.

Conclusions:

  • The MEVMDTFI-IRVM method offers a robust and effective solution for gearbox fault detection.
  • This novel approach enhances diagnostic accuracy, particularly in challenging signal conditions.
  • The study validates the superiority of MEVMDTFI-IRVM over existing fault detection techniques.