Fault diagnosis model based on modified evidence theory and neural network
Cuihua Wang · Jisuanji yingyong yanjiu · 2010
Directing to the low precision of single fault diagnosis systerm,this paper put forward the decision-level fusion fault diagnosis model which fusing neural network and D-S evidence.The method used D-S's evidence to deal with inaccuracy and fuzzy information,and evidence's basic belief assignment could be sloved by neural network.Proposed a new combination rule,which based on reallocation of the basic probability assigned to conflict focal elements.The method could solve the problem of conflicting evidences.The model could reduce the uncertainty of decision and greatly increase the precision of diagnosis.At last,the engine fault diagnosis example shows that the validity of the decision-level fusion fault diagnosis model.