Özet:

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 β 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.

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