Fault diagnosis of gearbox bearing of wind turbine based on ICEEMDAN-PE and GWO-LSSVM

In order to improve the accuracy of diagnosis, a fault diagnosis and research method of gearbox bearing of wind turbine based on ICEEMDAN-PE and GWO-LSSVM is proposed. The method firstly uses ICEEMDAN to decompose the sampled data signals, then uses PE to extract the features, and finally inputs the features into Grey Wolf algorithm (GWO) to optimize the Least Squares Support Vector Machine (LSSVM) diagnostic model to train the optimal parameters. A fault diagnosis model is built to identify common faults in gearbox bearings accurately. Combined with the simulation experimental data, the diagnosis accuracy is 97.67%, which proves the feasibility of the scheme, and provides an effective method for improving the accuracy of the gearbox bearing fault diagnosis