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Research on the EEMD-SE-IWTD Combined Noise Reduction Method for High-Speed Transient Complex Features in

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  • 1School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.

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Summary

This study introduces a new noise reduction technique for acceleration signals, effectively suppressing noise while preserving critical transient features. The method combines ensemble empirical mode decomposition (EEMD), sample entropy (SE), and improved wavelet threshold denoising (IWTD) for superior performance.

Keywords:
accelerationcombined noise reductionensemble empirical mode decomposition (EEMD)high-speed transient complex featuresimproved wavelet threshold denoising (IWTD)sample entropy (SE)

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Area of Science:

  • Signal Processing
  • Mechanical Engineering
  • Data Analysis

Background:

  • Traditional noise reduction methods struggle to balance noise suppression and transient feature preservation in acceleration signals.
  • High-speed transient data presents unique challenges for existing denoising techniques.

Purpose of the Study:

  • To propose a novel noise reduction method that effectively suppresses noise while preserving transient features in acceleration signals.
  • To address the limitations of traditional methods in handling high-speed transient data.

Main Methods:

  • Ensemble Empirical Mode Decomposition (EEMD) to decompose signals into intrinsic mode functions (IMFs) and a residual term.
  • Sample Entropy (SE) thresholding (SE = 0.3) to differentiate noise-dominated components from those with transient features.
  • Improved Wavelet Threshold Denoising (IWTD) applied to noise-dominated components, followed by signal reconstruction.

Main Results:

  • The proposed method achieves optimal noise reduction performance, validated by signal-to-noise ratio, root mean square error, and correlation coefficient.
  • Demonstrated effectiveness in preserving transient features crucial for signal analysis.
  • Successful validation using real multi-layer penetration acceleration signals.

Conclusions:

  • The novel EEMD-SE-IWTD method significantly enhances noise reduction performance in acceleration signals.
  • Preservation of transient features is achieved, supporting advanced analyses like penetration layer identification.
  • This approach offers a robust solution for denoising complex transient signals in mechanical and engineering applications.