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Super-resolution Imaging of Neuronal Dense-core Vesicles

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A Fault Feature Extraction Method Based on Improved VMD Multi-Scale Dispersion Entropy and TVD-CYCBD.

Jingzong Yang¹, Chengjiang Zhou², Xuefeng Li³

¹School of Dig Data, Baoshan University, Baoshan 678000, China.

Entropy (Basel, Switzerland)

|February 25, 2023

Summary

This study introduces an advanced method for extracting fault features from mechanical equipment signals. The technique effectively identifies fault characteristics even in noisy environments, improving machinery diagnostics.

Keywords:

CYCBD TVD VMD fault feature extraction rolling bearing

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Area of Science:

Mechanical Engineering
Signal Processing
Fault Diagnosis

Background:

Mechanical equipment faults are challenging to detect due to signal noise.
Extracting fault features is crucial for predictive maintenance and operational safety.

Purpose of the Study:

To develop an effective fault feature extraction method for mechanical equipment.
To address the challenge of submerged fault signals in noisy industrial environments.

Main Methods:

Proposed a novel method combining Variational Mode Decomposition (VMD) optimized by the Marine Predator Algorithm (MPA), multi-scale dispersion entropy, and TVD-CYCBD.
MPA optimized VMD parameters for signal decomposition.
TVD was used for denoising, followed by CYCBD filtering and envelope demodulation.

Main Results:

The method successfully decomposed and filtered fault signals.
Envelope spectrum analysis revealed clear multiple frequency-doubling peaks with minimal interference.
Verified effectiveness through simulation and actual fault signal experiments.

Conclusions:

The proposed fault feature extraction method demonstrates superior performance in identifying fault characteristics.
This technique enhances the reliability of fault diagnosis in complex industrial settings.