基于GMM工况辨识和DAE的风机齿轮箱状态监测

针对大型风电机组运行工况多变、数据量大的特点,提出一种将高斯混合模型(GMM)与深度自编码网络(DAE)相结合的风电机组齿轮箱状态监测方法。首先,基于GMM对风电机组运行工况进行辨识;然后,在各个子工况空间下,基于DAE建立正常运行状态下的齿轮箱油池温度模型,得到多工况阈值;最后,对DAE模型的重构误差进行分析,结合多工况阈值构建健康指数,实现风电机组齿轮箱的状态监测。以某台2 MW风电机组为实例进行验证,结果表明,该方法能够提前7天预警齿轮箱油池温度过高的故障;相对于基于DAE状态监测方法,在不影响在线监测时效性的情况下,该文所提方法能够提前约8 h预警潜在故障。

Condition monitoring of wind turbine gearbox based on GMM operational condition identification and DAE

In view of the variable operational conditions of large wind turbines and large amount of data,a wind turbine gearbox condition monitoring method based on Gaussian mixture model(GMM)and deep auto-encoder network(DAE)is proposed.Firstly,GMM is used to identify the operational conditions of wind turbine.Then,under each sub-condition space,DAE is used to establish the temperature models of the gearbox oil to obtain the multi-condition warning threshold.Finally,the reconstruction error of gearbox oil based on the DAE model is analyzed.The health index is constructed based on reconstruction error and multi-condition threshold to realize the condition monitoring.A 2 MW wind turbine is used as an example for verification.The results show that the proposed method can warn of potential faults 7 days in advance.Being compared with the method only based on DAE,without affecting the timeliness of online monitoring,this method can warn 8 h in advance.