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Vibration Signal Denoising Method Based on ICFO-SVMD and Improved Wavelet Thresholding.

Yanping Cui1, Xiaoxu He1, Zhe Wu1

  • 1School of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China.

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|January 28, 2026
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Summary
This summary is machine-generated.

A novel joint denoising method effectively removes noise from rotating machinery vibration signals. This approach enhances diagnostic reliability by preserving weak fault features for improved machinery health monitoring.

Keywords:
denoisingimproved cordyceps fungus optimization algorithm (ICFO)improved wavelet thresholdingsuccessive variational mode decompositionvibration signals

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Area of Science:

  • Mechanical Engineering
  • Signal Processing
  • Computational Intelligence

Background:

  • Rotating machinery generates non-stationary, multi-component vibration signals.
  • Strong background noise often masks critical fault features, reducing diagnostic accuracy.
  • Existing denoising methods struggle with complex noise interference.

Purpose of the Study:

  • To develop an advanced joint denoising method for contaminated vibration signals.
  • To enhance the reliability of fault diagnosis in rotating machinery.
  • To improve the detection of weak fault signatures.

Main Methods:

  • A hybrid approach combining improved cordyceps fungus optimization algorithm (ICFO), successive variational mode decomposition (SVMD), and wavelet thresholding.
  • ICFO adaptively optimizes SVMD parameters using enhanced initialization and mutation strategies.
  • Signal decomposition, component classification using Pearson correlation, and adaptive wavelet thresholding for reconstruction.

Main Results:

  • The proposed method significantly improved signal-to-noise ratio (SNR) and reduced root mean square error (RMSE) across various noise levels.
  • Achieved superior performance compared to VMD, SVMD, VMD-WTD, CFO-SVMD, and WTD in simulations.
  • Demonstrated effectiveness on real-world bearing and gearbox data, preserving fault frequencies with low residual variance.

Conclusions:

  • The joint denoising method offers a robust solution for analyzing noisy vibration signals in rotating machinery.
  • It effectively suppresses noise while retaining essential fault information for accurate diagnostics.
  • This technique enhances the potential for early fault detection and predictive maintenance.