Blind Signal Processing And Its Application Based On PCA And ICA

Aim to the limitation of traditional signal processing, the theories and methodologies based on statistical signal processing, which are used to extrat the independent components from mixture signals, have been researched. To meet the needs of engineering, the rapidity, stability and self-adaptability of the methodology have been researched. Faces to the real signal of engineering, the applications of independent component analysis (ICA) have been researched. First, the theories and methodologies of principal component analysis (PCA) and independent component analysis have been discussed. The relationships of independent and irrelevance have been explained. The white theory and its application in engineering have been expounded. The feature of decorrelation based on whitening has been researched. The result shows the correlative level of signals can be reduced mostly after whitening them. The contributiveness-based methodology of principal component selection has been researched. The number of principal component can be determined according this principle. Applied this theory on real signal of engineering, the minimal number of sensor can be determined by using. The theory and methodology for signal whitening based on PCA have been researched. The contrast functions have been analyzed, the selected methodology for contrast function has been summarized, and several contrast functions have been proposed. The key of independent estimation based on neural calculating is gradient decent. On the based of stochastic gradient decent algorithm, an improved nonlinear ICA algorithm has excellent effect for the signals that have the same sign of kurtosis. Aim to the problem of dis-separating for the same frequency mixed signal, the simulation for blind source separation based on PCA whitening has been researched, and applying the method on real engineering signal —the roll machine vibration signals disturbed by the same frequency noises on signal transmission. The result shows that the blind source separation can excellently separate the signals, which their components are overlapped in frequency domain. Fixed-point algorithm based on kurtosis contrast function and others fixed-point algorithms based on high-order nonlinear contrast function. On the fundamental of fixed-point algorithm proposed by Hyvarien, the other nonlinear contrast functions have been proposed in this thesis, and the reliability improved methods has been proposed also. The simulations for different distributed components have been processing by using different nonlinear contrast functions. Aim to the un-determination of order of the component occurs in separating, the order resorting has been researched, and a order resorting algorithm of component has been proposed. The character of main driving system of rolling machine and its inherent frequency has been analyzed. The vibrant model of the main driving system has been created. The mechanism of failed of couple, which is damageable component in the main driving system of rolling machine, has been pointed out. A set of monitoring system of the main driving system of roll machine has been developed. This monitoring system is developed by using DataSocket programming. The communications between workstations and server can be over Internet. A self-adaptive filter has been designed to smooth the vibration signals that are disturbed by power pause in the space. A set of FM wireless communication for torsion monitoring has been designed and been applied also. This torsion monitoring system is easy to mount. The problem, which the signals disturbed by same frequency noises over the transmission in space can't be demodulated correctly, has been solved by using ICA. This thesis indicated the ICA can solved the problem, which can't be completed correctly by using traditional signal processing methods.