A Method Combining Fractal Analysis and Single Channel ICA for Vibration Noise Reduction

Quanbo Lu,Mei Li,Marcello Vanali

doi:10.1155/2021/5583587

Abstract

Aiming at the problem that real engineering vibration signals are interfered by strong noise, this paper proposes a method combining single channel-independent component analysis (SCICA) and fractal analysis (FD) to reduce the effect of noise on the time-frequency analysis of vibration signals. First, phase space reconstruction is performed on the vibration signal to make the proper input for ICA algorithm. The original is then decomposed into several component signals. The fractal dimension of each component signals is calculated to determine whether the signal should be considered noise. Noisy component signals are then processed by wavelet denoising. Finally, the output signal after noise reduction is reconstructed using the filtered “right” component signals. This paper uses the method to analyze real noisy vibration signal. Experimental results show the effectiveness of the proposed method.

Highlights

To solve this problem, this paper proposes a vibration signal noise reduction method based on single channel-independent component analysis (SCICA) and FD
This paper proposes a vibration signal noise reduction method based on SCICA and FD
The component signal required for filtering is selected using fractal dimensions, different denoising methods are performed on the selected component signals, and the signals are reconstructed. e results show that the proposed algorithm greatly suppresses noise and effectively avoids the loss of useful information

Summary

Quanbo Lu and Mei Li

Aiming at the problem that real engineering vibration signals are interfered by strong noise, this paper proposes a method combining single channel-independent component analysis (SCICA) and fractal analysis (FD) to reduce the effect of noise on the time-frequency analysis of vibration signals. Guerrero has proposed an adaptive noise reduction method that combines ICA and recursive least square (RLS) for processing EEG signals [14]. This method filters all source signals after ICA decomposition. If useful information is lost because of filtering, the analysis and identification of vibration signals will be ambiguous To solve this problem, this paper proposes a vibration signal noise reduction method based on SCICA and FD.

Methods

Consider the FD of each component signal

Sampling point

Mixed signal

Sorted sequence