Rough support vector machine and its Application to aero-engine fault diagnosis

support vector machine is a kind of new machine learning method.This method has good generality capability and better classification accuracy.But when solve real problem using support vector machine,its computation rate is slow and its efficiency is low.Introduce a kind of method named rough support vector machine (RSVM) that improves the real-time character of prediction system based on SVM in this paper. RSVM has high classification accuracy with much less attributes ,which means less sensors and less cost.And it keeps certain redundant attributes to have high fault diagnosis accuracy in the case of lost information caused by sensor fault.RSVM increases classification accuracy with good generalization performance.The numerical experiments for aero -engine fault diagnosis show the effectiveness of the proposed algorithm.