Separation Of Vehicle Vibration Signals Based On Blind Source Separation

The NVH of the vehicle directly affects the comfort of the vehicle,and the vibration signal of the vehicle is composed of multiple vibration sources.Identifying vehicle vibration source is one of the important research contents to improve vehicle comfort.Blind source separation algorithm has great research space in identifying mechanical vibration,fault diagnosis and noise treatment.In this paper,the blind source separation algorithm is used to separate the vehicle vibration signals.An appropriate signal denoising method is proposed,and the vibration signal blind separation algorithm is studied to provide a new method for the vehicle vibration signal processing.The main contents are as follows:The theoretical knowledge of the blind source separation is sorted and organized.The mathematical model and mathematical knowledge of blind source separation are introduced systematically,and the basis of statistical independence of random variables in blind source separation algorithm is summarized,and the basic conditions and evaluation criteria of blind source separation are described.The blind separation algorithms,such as FastICA,SOBI,JADE and CuBICA,are studied,the vibration signal of the engine is simulated with the excitation signal of the pavement and the mixture is mixed instantaneously and convolution respectively,using the above four algorithms for blind separation simulation,the results show that the blind separation algorithms based on instantaneous mixing and convolution mixing FastICA,SOBI,JADE and CuBICA all have good separation performance.Analysis of defects in EEMD and wavelet denoising respectively,proposes the EEMD and wavelet half soft threshold denoise algorithm for noise reduction of vehicle vibration signal,and compared with EEMD wavelet denoising method,semi soft threshold denoising method,EMD and wavelet hard threshold denoising method and combined EMD and wavelet soft threshold denoising method combined,through simulation analysis and experiment the research verified the feasibility of the proposed algorithm.The engine vibration signal and pavement excitation signal were collected through bench test and pavement experiment,EEMD and wavelet semi soft threshold denoising are used to study vehicle vibration signals based on CuBICA blind separation based on instantaneous mixing and convolution.The results show that the blind separation algorithm based on convolution mixing should be selected to deal with the mixed signal when the engine vibration signal and road excitation signal are collected simultaneously.