Gearbox fault diagnosis method based on the fusion of EEMD and improved Elman-NN

Abstract

In connection with the complex operating conditions of gearbox, multiple vibration excitation sources, and difficulty in extracting vibration signal fault features, a novel method of gearbox fault diagnosis is proposed. based on the fusion of EEMD and improved Elman neural network (Elman-NN) is developed. The wavelet packet is utilized to denoise the collected vibration signals of four different types of gearboxes: broken teeth, cracks, wear, and normal, and then use the EEMD method to decompose the denoised vibration signals, and use the correlation coefficient criterion means to carry out the IMF pseudo component elimination, and then get a more effective signal. Calculate the energy feature of the effective signal and use it as the enter feature of the Elman-NN. Based on standard Elman-NN, a self-feedback factor is added to construct a reformed Elman-NN. Experimental results indicate that compared with the standardized Elman-NN, the improved Elman-NN has higher diagnostic accuracy and diagnostic efficiency.

Identification of Vibration Signal for Residual Pressure Utilization Hydraulic Unit Using MRFO-BP Neural Network

“…e vibration frequency distribution of Gaussian white noise is uniform, which can effectively reduce the problem of insufficient endpoint effect and suppress the phenomenon of modal aliasing [30]. Based on the original EMD method, the EEMD method is proposed, which is realized by adding white noise with equal amplitude to the original signal for many times, and using EMD to decompose the signal after adding white noise [31,32]. In order to reduce the influence of white noise on the signal to be measured, the average value of the IMF components obtained by EMD decomposition is used as the final decomposition result.…”