Since the system of wind turbine is quite complex,the relationship between faults and phenomena is not simple or linear,the diagnostic requirements could not be met by single detection.Aimed at this problem,the information fusion theory of Wireless Sensor Network was applied in wind turbine's state monitoring and fault diagnosis,which made information fusion separately in two levels of signal level and characteristics level of a large amount of collected data.By using self-adaptive weighting fusion algorithm,the data redundancy and transmission energy consumption of network were reduced,and using the Gauss membership function,the basic probability assignment was obtained,which enhanced the D-S evidence theory data reliability and improve the ability of fault identification.Finally,a simulation experiment of fault diagnosis was held on gearbox of wind turbine.The experimental results prove that the method has a high diagnostic accuracy,and obviously improves diagnostic reliability.