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Parameter-Adaptive TVF-EMD Feature Extraction Method Based on Improved GOA.

Chengjiang Zhou1,2, Zenghui Xiong1,2, Haicheng Bai1,2

  • 1School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China.

Sensors (Basel, Switzerland)
|October 14, 2022
PubMed
Summary
This summary is machine-generated.

A new adaptive method using improved grasshopper optimization (IGOA) enhances time-varying filtering empirical mode decomposition (TVF-EMD) for accurate signal feature extraction. This approach overcomes common decomposition issues and improves mechanical fault diagnosis.

Keywords:
EEMI indexIGOATVF-EMDbearingsignal decomposition

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Area of Science:

  • Signal Processing
  • Optimization Algorithms
  • Mechanical Engineering

Background:

  • Accurate signal decomposition and feature extraction are crucial for mechanical fault diagnosis.
  • Traditional methods like empirical mode decomposition (EMD) suffer from issues such as under-decomposition, over-decomposition, and modal aliasing.
  • Adaptive parameter selection is essential for optimizing decomposition performance.

Purpose of the Study:

  • To propose a parameter-adaptive time-varying filtering empirical mode decomposition (TVF-EMD) method for feature extraction.
  • To improve the Grasshopper Optimization Algorithm (GOA) by addressing its local optimum problem and enhancing its search capabilities.
  • To adaptively determine the bandwidth threshold and B-spline order for TVF-EMD using an optimized approach.

Main Methods:

  • Introduced an improved Grasshopper Optimization Algorithm (IGOA) with a nonlinear decreasing strategy for dynamic coefficient adjustment.
  • Utilized Energy Entropy Mutual Information (EEMI) as the objective function to evaluate mode energy distribution and signal dependence.
  • Optimized TVF-EMD parameters using IGOA to achieve optimal signal matching and feature frequency extraction via kurtosis analysis.

Main Results:

  • IGOA demonstrated improved global and local search ability, stability, and a better balance between exploration and development compared to standard GOA.
  • The proposed parameter-adaptive TVF-EMD method accurately separated signal modes with physical meaning, outperforming EEMD, VMD, fixed-parameter TVF-EMD, and GOA-TVF-EMD.
  • The method effectively resolved under-decomposition, over-decomposition, and modal aliasing issues inherent in TVF-EMD.

Conclusions:

  • The parameter-adaptive TVF-EMD method based on IGOA offers superior decomposition performance for signal analysis.
  • This technique accurately separates frequency components and extracts feature information, addressing limitations of existing methods.
  • The proposed approach holds significant practical value for mechanical fault diagnosis applications.