中文摘要：

由于风电机组系统相当复杂，故障原因及其现象不成简单或线性对应关系，单一检测不能够满足诊断需要。针对这一问题，将无线传感器网络（WirelessSensorNetwork）中信息融合的理论和方法应用于风电机组状态监测和故障诊断中，使采集到的海量数据分别进行信号层与特征层两个层次的信息融合，运用自适应加权融合算法降低网络的数据冗余和传输能量消耗，利用高斯隶属度函数获得基本概率的赋值，提高了D—S证据理论数据的可靠性，改进的证据组合方法提高了故障识别能力。最后，对风电机组齿轮箱的故障诊断进行仿真实验，实验结果验证了该方法具有较高的诊断精度，明显提高诊断的可信度。

英文摘要：

Since the system of wind turbine is quite complex, the relationship between faults and phenomena is not simple or lin- ear, the diagnostic requirements could not be met by single detection. Aimed at this problem, the information fusion theory of Wire- less 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 i- dentification. 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.