Method and device for fault diagnosis of wind turbine pitch bearing based on neural network

Abstract

A method and a device for fault diagnosis of wind turbine pitch bearing based on neural network, the method includes: measuring the signal strength at different points of a sensor and different rolling angles, determining an optimal measurement rolling angle of a blade and sensor point arrangement, blocking the blade at the optimal rolling angle to collect the pitch vibration data, further processing the collected vibration data into a dataset, constructing a neural network model, using the collected dataset to train the network, and deploying the trained network to PLC for real-time dynamic monitoring of the wind turbine; the device includes the vibration acceleration sensors, a vibration data acquisition card and a programmable logic controller (PLC). The present disclosure can realize the fast, real-time and accurate monitoring of the health status of the pitch bearing.