关键词: FastICA算法, 初值敏感, 最速下降法, 松弛因子, 轴承故障诊断

Abstract: The FastICA analysis method is optimized by adopting fast decent method and successive over relaxation factor to pre-process the initial value of de-mixing matrix. The development aims to improve the convergence property and avoid the uneven phenomenon. Firstly, the better solutions to de-mixing matrix is selected by the fast decent method with the fastest convergence rate. Then, it introduces successive over relaxation factor to limit the decent property of the objective function according to the given norm. The final initial value is obtained by these two steps, and the combination of these two steps can ensure algorithm convergence. The optimized FastICA algorithm is applied to fault diagnosis of bearings, according to the iteration time and curve shape of the convention. It is testified that the optimized method is superior to the convention one in terms of stability and speed, and inherits the separation performance of convention FastICA method.

Key words: FastICA algorithm, initial value sensitivity, fast decent method, successive over relaxation factor, bearing fault diagnosis