Gearbox Fault Prediction of Wind Turbine Based on Improved NEST Model

Abstract

This paper studies a fault prediction method for wind turbine gearbox. It uses grey relation analysis to get modeling variables, and makes sample data getting good integrity and redundancy by similarity analysis. Thus it gets the reduced process memory matrix, and trains the improved nonlinear state estimation (NEST) model. When the gearbox fails, the model residual will exceed the threshold value, and the model will give an early warning. Combined with the actual operation data of a wind turbine, the effectiveness and accuracy of the improved model are verified.