Multisensor network information fusion method was used to diagnose the aeroengine with early vibration fault symtom

and several vibration sensors had been fixed on different positions of the aeroengine to build the multisensor network.However the information collected from different sensors would highly conflicts when early vibration fault happens

and the information fusion result get by Dempster-Shafer rule would be unreasonable

so Dezert-Smarandache theory (DSmT) was applied to solve the problem.In the early vibration fault diagnosis system proposed in this paper

firstly

a method based on the intrinsic mode functions (IMF) energy entropy was used to extract the signal’s feature;secondly

basic belief assignment function was constructed based on the output of the back propagation (BP) neural network;lastly

DSmT combination rule was used to combine the different evidences and make the final decision.Two examples suggest the approach is available to solve the problem of high-conflict information fusion when early vibration fault happens

and the diagnosis results are reliable and effective.