Fusion Diagnosis of Engine Wearing Fault Based on Neural Networks and D-S Evidence Theory

Four common oil analysis techniques, namely Ferrography analysis,Spectrometric analysis,Particle count analysis,and Oil chemical-physics analysis,were used together with the engine test data to develop the fusion diagnosis method of engine wearing fault based on Neural Networks (NN) and D-S evidence theory.Firstly,according to standard wear limit,original data were transformed into BOOL value.Then,each sub-NN structure was established,and their training samples were obtained based on expert experience.After each sub-NN was trained successfully,the intermediate diagnosis results were obtained through each sub-NN.Finally,the NN diagnosis results are used as the basic probability distribution value to each fault mode,and the D-S evidence theory is applied,and the final fusion diagnosis results are obtained.An example was used to verify the method presented in this paper.