Fault Diagnosis of Wind Turbine Gearbox based on EEMD Wavelet Threshold Denoising and CS-BP Neural Network - Wang Hongjun; Zhao Yuanlu; Zhao Hui; Yue Youjun (Jixie chuandong)
For the fault diagnosis of the pitting,gear wear and tooth breakage in the gearbox of wind turbines,a fault diagnosis method based on EEMD and wavelet threshold denoising and cuckoo search to optimize BP neural network is proposed. The EEMD decomposition and wavelet threshold denoising method are used to preprocess the fault vibration signals and suppress the noise interference in the original vibration signals. The cuckoo search is used to optimize the BP neural network to diagnose the preprocessed signals. The wavelet threshold can better denoise the high frequency components in the EEMD decomposition,and the CS-BP neural network has accurate pattern recognition accuracy and excellent global optimization ability. The simulation shows that the diagnostic method has good accuracy,speed and success rate,and is of high use significance.