关键词: 数据采集与监视控制, 深度信念网络, 温度建模, 状态监测

Abstract: A condition monitoring method of wind turbine main bearings was proposed based on DBN. To reduce the modeling difficulties and decrease the training time, a correlation coefficient method was firstly applied to select the modeling variables. Further, a DBN temperature model of the normal behaviors of the main bearings was established and the main bearing temperature was predicted. This model overcomes the shortcomings of traditional neural network that randomly initializes the network weights and easily to fall into the local minimum thus may effectively improve the prediction accuracy of main bearing temperature. Then exponentially weighted moving average was used to analyze the residual errors of main bearing temperature and the failure threshold was determined by kernel density estimation method. Lastly, the main bearing failure was simulated based on the measured SCADA data. The experimental results show that the proposed method may effectively realize the abnormal condition monitoring of the main bearings than that of the traditional prediction methods.

Key words: supervisory control and data acquisition(SCADA), deep belief network(DBN)；temperature modeling, condition monitoring