中文摘要：

针对风电机组齿轮箱故障诊断技术的不足，提出一种基于LVQ神经网络的故障诊断方法，利用小波分析方法对某风电机组齿轮箱正常状态、磨损故障和断齿故障状态下的振动信号进行降噪处理，在时域和频域内提取了5个特征参数对所建立的模型进行训练。为了检验模型的实际诊断能力，与标准BP神经网络的诊断结果进行对比。仿真结果表明：基于LVQ神经网络的故障诊断速度更快、准确率更高、泛化能力更强，验证了所提出方法的实用性和有效性。

英文摘要：

In view of the deficiency in fault diagnosis technique of wind turbine gearbox,a fault diagnosis method based on LVQ neural network is proposed. Wavelet analysis is used to de-noise the vibration signals of a wind turbine gearbox in its nor-mal condition,wear fault condition and tooth breakage condition. Five characteristic parameters are extracted in the time domain and frequency domain to train the established model. To test its practical diagnosis ability,the diagnosis result of the model is compared with that obtained by a standard BP neural network. The simulation results show that the diagnosis method based on LVQ neural network has a faster diagnosis speed,higher accuracy and stronger generalization ability. The method proposed in this paper was verified to be practical and effective.