Condition Monitoring of Wind Turbine Gearbox Bearing Based on Deep Learning Model

Abstract

Wind turbines condition monitoring and fault warning have important practical value for wind farms to reduce maintenance costs and improve operation levels. Due to the increase in the number of wind farms and turbines, the amount of data of wind turbines have increased dramatically. This problem has caused a need for efficiency and accuracy in monitoring the operating condition of the turbine. In this paper, the idea of deep learning is introduced into wind turbine condition monitoring. After selecting the variables by the method of the adaptive elastic network, the convolutional neural network (CNN) and the long and short term memory network (LSTM) are combined to establish the logical relationship between observed variables. Based on training data and hardware facilities, the method is used to process the temperature data of gearbox bearing. The purpose of artificial intelligence monitoring and over-temperature fault warning of the high-speed side of bearing is realized efficiently and conveniently. The example analysis experiments verify the high practicability and generalization of the proposed method.