Blind Source Separation Method On Gearbox Vibration Signals

This paper first systematically expounds the basic theory of blind source separation, the analysis and research is mainly focused on the linear instantaneous blind source separation theory and post nonlinear blind source separation related theory. In the study of linear instantaneous blind source separation, by method of fastICA based on kurtosis and fastICA based on negative entropy and non gaussian of mixed signal successfully achieved the separation of mixed-signals. Through the similarity coefficient test the separation accuracy of FastICA method based on kurtosis and FastICA method based on negative entropy. In the framework of information theory, applied the theory of information entropy to separate the mixed-signals successfully, through the value of PI and similarity coefficient the separation accuracy of the information entropy method respectively. Finally, according to the sampling data of different length, compared the FastICA method and maximum likelihood estimation algorithm efficiency, When the data sampling points increased to 10000, the time of maximum likelihood estimation method is about 6 times of Fast ICA method. In the study of the post nonlinear blind source separation based on artificial neural network, the feedforward neural network combined with the information entropy theory, build up the model of post nonlinear blind source separation. In the post nonlinear blind source separation model, the feedforward neural networks which were the classical BP network and RBF network were applied on nonlinear mixed signals separation, then with information entropy as the independence criterion of the neural network output signals, implement three-channel nonlinear mixed signal separation, verifying the nonlinear blind source separation method is effective.Finally, through application of linear blind source separation method, successfully separated gear box vibration signals at the case of 950 r/min and 1000 r/min, smoothly identified the failure of the gearbox bearing inner ring and gear tooth broke down. Blind source separation(BSS) method can effectively separate the mechanical vibration signals, and has significant advantages in dealing with a weak signals.