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

## TL;DR

A new vibration signal denoising method is proposed that improves fault detection in noisy machinery by combining optimization algorithms and wavelet thresholding.

## Contribution

A novel joint denoising framework using ICFO-SVMD and improved wavelet thresholding for nonlinear, non-stationary vibration signals.

## Key findings

- The proposed method outperforms existing techniques in SNR, RMSE, RVR, and SER metrics.
- It preserves transient fault features better in noisy bearing and gearbox vibration data.
- The method is effective across a wide range of noise levels (SNR = 1–20 dB).

## Abstract

What are the main findings?
A joint denoising framework (ICFO–SVMD–improved wavelet thresholding) is proposed which adaptively optimizes SVMD parameters and sub-band thresholds for nonlinear, non-stationary vibration signals.Simulation and experimental results on bearing and gearbox vibration data show that the proposed method outperforms VMD, SVMD, VMD–WTD, CFO–SVMD, and traditional wavelet denoising in terms of SNR, RMSE, RVR, and SER while better preserving transient fault features.

A joint denoising framework (ICFO–SVMD–improved wavelet thresholding) is proposed which adaptively optimizes SVMD parameters and sub-band thresholds for nonlinear, non-stationary vibration signals.

Simulation and experimental results on bearing and gearbox vibration data show that the proposed method outperforms VMD, SVMD, VMD–WTD, CFO–SVMD, and traditional wavelet denoising in terms of SNR, RMSE, RVR, and SER while better preserving transient fault features.

What are the implications of the main findings?
The proposed method provides a robust preprocessing tool for vibration-based condition monitoring under strong noise, improving the reliability of fault feature extraction and fault diagnosis in rotating machinery.The ICFO–SVMD–WTD framework is generic and can be extended to other noisy engineering signals that require joint decomposition and adaptive thresholding.

The proposed method provides a robust preprocessing tool for vibration-based condition monitoring under strong noise, improving the reliability of fault feature extraction and fault diagnosis in rotating machinery.

The ICFO–SVMD–WTD framework is generic and can be extended to other noisy engineering signals that require joint decomposition and adaptive thresholding.

Non-stationary, multi-component vibration signals in rotating machinery are easily contaminated by strong background noise, which masks weak fault features and degrades diagnostic reliability. This paper proposes a joint denoising method that combines an improved cordyceps fungus optimization algorithm (ICFO), successive variational mode decomposition (SVMD), and an improved wavelet thresholding scheme. ICFO, enhanced by Chebyshev chaotic initialization, a longitudinal–transverse crossover fusion mutation operator, and a thinking innovation strategy, is used to adaptively optimize the SVMD penalty factor and number of modes. The optimized SVMD decomposes the noisy signal into intrinsic mode functions, which are classified into effective and noise-dominated components via the Pearson correlation coefficient. An improved wavelet threshold function, whose threshold is modulated by the sub-band signal-to-noise ratio, is then applied to the effective components, and the denoised signal is reconstructed. Simulation experiments on nonlinear, non-stationary signals with different noise levels (SNR = 1–20 dB) show that the proposed method consistently achieves the highest SNR and lowest RMSE compared to VMD, SVMD, VMD–WTD, CFO–SVMD, and WTD. Tests on CWRU bearing data and gearbox vibration signals with added −2 dB Gaussian white noise further confirm that the method yields the lowest residual variance ratio and highest signal energy ratio while preserving key fault characteristic frequencies.