Studies on Blind Separation Method of Nonlinear Mixture Machine Faults Based on Kernel Independent Component Analysis

A nonlinear mixture blind separation method of the mechanical fault sources based on kernel independent component analysis is proposed. In the proposed method, the signal is transformed from the low-dimensional nonlinear original space into a high-dimensional linear feature space by the kernel function, so that nonlinear mixture mechanical fault sources can be separated by the linear ICA method. The simulation result shows that the proposed method is superior to the traditional ICA method in processing nonlinear mixed blind separation problem. Finally the proposed method is applied to the blind separation of the bearing fault. The experiment result further verifies the validity of the proposed method.