Single-channel vibration signal blind source separation method

Abstract

The invention discloses a single-channel vibration signal blind source separation method, including the following steps: S1. multi-path original single-channel vibration observation signals arriving from paths are subjected to in-phase superposition, and then the superposed original single-channel vibration observation signal is subjected to denoising treatment, thereby obtaining a denoised single-channel vibration observation superposed signal; S2. wavelet decomposition and reconstruction are utilized to convert the denoised single-channel vibration observation superposed signal into a multi-channel vibration observation signal; and S3. a blind source separation algorithm based on time frequency analysis is utilized to analyze the multi-channel vibration observation signal, thereby obtaining an estimated signal of the original single-channel vibration observation signal, and realizing single-channel vibration signal blind source separation. The single-channel vibration signal blind source separation method provided by the invention can enhance signal energy of the original single-channel vibration observation signal, can reduce noise, enables a blind source separation effect to be relatively good, and effectively improves the signal to noise ratio of the signal, and the method is simple, is convenient to use, is low in cost.

Description

technical field [0001] The invention relates to the technical field of single-channel vibration signals, in particular to a method for blind source separation of single-channel vibration signals. Background technique [0002] Blind source separation only separates the source signal from the sensor observation signal, which is a promising signal processing technology. In recent years, blind source separation methods have been more and more used in the field of vibration signal processing. According to blind source separation itself In vibration signal separation, it is generally assumed that the number of observed signals is not less than the number of vibration source signals, but this assumption cannot be realized in engineering. Usually, the blind source separation problem in which the number of observed signals is smaller than the number of source signals is called underdetermined Blind source separation, underdetermined blind source separation methods mainly include: sou...

AI Technical Summary

Problems solved by technology

[0002] Blind source separation only separates the source signal from the sensor observation signal, which is a promising signal processing technology. In recent years, blind source separation methods have been more and more used in the field of vibration signal processing. According to blind source separation itself In vibration signal separation, it is generally assumed that the number of observed signals is not less than the number of vibration source signals, but this assumption cannot be realized in engineering. Usually, the blind source separation problem in which the number of observed signals is smaller than the number of source signals is called underdetermined Blind source separation, underdetermined blind source separation methods mainly include: source number estimation and separation algorithm in underdetermined blind separation, underdetermined blind separation method based on singular value decomposition, underdetermined blind separation method based on potential function, etc. However, These existing algorithms are basically based on the sparse representation of the source signal. When the sparsity of the signal is not good, the effect of blind source separation will be relatively poor.

[0003] Most of the existing blind source separation methods for single-channel vibration signals cannot enhance the signal energy of the original single-channel vibration observation signal, resulting in an inconspicuous effect of blind source separation, and cannot obtain a good noise cancellation effect, effectively improving the signal-to-noise signal Compare