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Updated: Sep 30, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Gearbox Fault Diagnosis Based on Improved Variational Mode Extraction.

Yuanjing Guo1, Shaofei Jiang2, Youdong Yang1

  • 1Zhijiang College, Zhejiang University of Technology, Shaoxing 312030, China.

Sensors (Basel, Switzerland)
|March 10, 2022
PubMed
Summary

This study introduces an improved variational mode extraction (VME) method for detecting weak fault features in gearbox vibrations. The new approach enhances gearbox fault diagnosis by effectively extracting impulse features, outperforming traditional methods.

Keywords:
SDE indexfault feature extractiongearboxshort-time Fourier transformvariational mode extraction

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Area of Science:

  • Mechanical Engineering
  • Signal Processing
  • Condition Monitoring

Background:

  • Gearbox health is critical for rotating machinery reliability.
  • Gear tooth failure generates weak, hard-to-extract impulse features in vibration signals.

Purpose of the Study:

  • To propose a novel approach for gearbox fault feature extraction and diagnosis.
  • To address the challenge of extracting weak impulse features caused by gearbox tooth failure.

Main Methods:

  • Developed an improved variational mode extraction (VME) method.
  • Utilized short-time Fourier transform (STFT) to determine VME parameters.
  • Introduced a new index: standard deviation of differential values of envelope maxima positions (SDE).

Main Results:

  • The improved VME approach successfully extracted weak impulse features from vibration signals.
  • Verified effectiveness using simulation and experimental gearbox test bench data.
  • Demonstrated superior performance compared to the variational mode decomposition (VMD) method.

Conclusions:

  • The proposed VME-based method offers a robust solution for gearbox fault diagnosis.
  • The novel parameter setting strategy enhances the VME technique's applicability.
  • This approach improves the reliability of condition monitoring for rotating machinery.