Blind separation of mechanical fault sources based on variational Bayesian mixture of independent component analysers
Jingang Chen · Machinery Design and Manufacture · 2012
Decomposing and representing data using independent component analysers(ICA)assumes that the whole data distribution is adequately described by one coordinate frame.However,if the observed data consists of various self-similar,non-Gaussian manifolds,enforcing a single,global representation is not appropriate and will produce a sub-optimal representation.In order to make up the lack of independent component analyser in blind sources separations,blind separation of mechanical fault sources based on variational Bayesian mixture of independent component analysers is presented based on variational Bayesian theory in this paper.Considering the source signals coming from multiple frames,the method creats a mixture model of independent component analysers in multiple frameworks for learning the observed signals and separating them.The experimental results show that the method proposed in this paper is very effective.