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Impact feature recognition method for non-stationary signals based on variational modal decomposition noise reduction

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This study introduces a novel method using Variational Mode Decomposition (VMD) and a Whale Optimization Algorithm (WOA)-optimized Support Vector Machine (SVM) for accurate non-stationary dynamic signal analysis and recognition.

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Area of Science:

  • Engineering
  • Signal Processing
  • Machine Learning

Background:

  • Distinguishing key information in non-stationary dynamic signals is challenging in fields like fault detection and geological exploration.
  • Existing methods often struggle with the complexity of these signals.

Purpose of the Study:

  • To propose a robust algorithm for classifying and recognizing non-stationary dynamic signals.
  • To enhance the accuracy and efficiency of signal analysis in engineering applications.

Main Methods:

  • Variational Mode Decomposition (VMD) to break down signals into intrinsic mode functions (VIMFs).
  • Signal reconstruction and modal filtering based on correlation coefficients.
  • Whale Optimization Algorithm (WOA) to optimize Support Vector Machine (SVM) parameters.
  • Classification and recognition of impact and vibration signals using the VMD-WOA-SVM model.

Main Results:

  • The proposed VMD-WOA-SVM method demonstrated faster convergence compared to other approaches.
  • Achieved a high recognition precision of 96.66% for non-stationary dynamic signals.
  • Effectively identified key information within complex signals through modal filtering and optimized SVM classification.

Conclusions:

  • The VMD-WOA-SVM algorithm offers a superior approach for analyzing non-stationary dynamic signals.
  • This method significantly improves accuracy and efficiency in signal classification and recognition tasks.
  • The study validates the effectiveness of combining VMD with WOA-optimized SVM for engineering applications.