Single-channel blind source separation for vibration signals based on TVF-EMD and improved SCA

Abstract

A novel single-channel blind source separation method is studied. This method not only addresses the problem that the traditional blind source separation method depends on the number of sensors but also achieves the purpose of effectively identifying the modal parameters. Firstly, the time varying filter for empirical mode decomposition (TVF-EMD) method is used to decompose the one-dimensional observation signal into intrinsic mode functions (IMFs) with different scale characteristics. Then, the similarity measure of the probability density function between each IMF and the one-dimensional observation is calculated. According to the threshold, several sensitive IMFs are selected and a new observation is constructed with random partial reconstruction to form a two-dimensional matrix. Finally, the reconstructed signals are separated by the improved sparse component analysis (SCA) method based on energy detection in the frequency domain. The simulation results demonstrate that the proposed method can separate not only simulated vibration signal, but also damping sinusoidal signal effectively from the single-channel while the source number is unknown. And the parameters of natural frequencies and damping ratios of modal responses can be accurately identified in the test of the measured cantilever beam hammering.