Prediction of Spindle Bearing Temperature Rise and Brake Wear of Wind Turbines Based on Kalman Wavelet Neural Network

摘要

Wind turbine spindle and brake are important parts of the wind turbine, they are also key components prone to failure. Mainly manifested in spindle bearing temperature rise and brake wear. Based on the temperature of the main shaft bearing, the brake wear is as the research object, is proposed based on wavelet neural network combined with kalman filtering network model of the spindle bearings for large wind turbines and predict the working state of the brake, the simulation results verify the validity of the prediction method, can significantly improve the security of the spindle to work.