Negentropy and gradient iteration based fast independent component analysis for multiple random fault sources blind identification and separation

Abstract

In order to separate multiple random fault sources only from mixed vibration measurement response signals of mechanical system, Negentropy and Gradient iteration based fast independent component analysis (FastICA) is applied for blind signal separation (BSS). After finding the association between independent components (ICs) matrix and multiple random fault sources, multiple random fault sources identification problem is turned into ICA of the stationary random vibration response signals of mechanical system. This method uses negative entropy maximization as criterion of independence, Gradient iteration as optimization method to extract random fault sources one by one. Simulation experiment results verified that this method could identify and separate multiple random fault sources only from mixed vibration measurement response signals of mechanical system correctly and effectively.