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 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.
Highlights
As the core component of mechanical transmission system, gearbox is widely used in various mechanical equipment [1]
Ayodeji [4] introduced an Elman-neural network (NN) to perform nuclear power plant fault diagnosis, and the results show that the diagnosis method is feasible
Based on the advantages of the EEMD method and the shortcomings of the standard Elman-NN, this paper puts forward a gearbox fault diagnosis means based on the fusion of EEMD and the improved Elman-NN
Summary
As the core component of mechanical transmission system, gearbox is widely used in various mechanical equipment [1]. In order to solve this problem better, Huang proposed an improved EMD method, namely EEMD (Ensemble Empirical Mode Decomposition) method [3]. Chemseddine [5] proposed a novel fault diagnosis method for gearbox system based on the fusion of HEWT-SVD and Elman-NN. Baraldi [6] developed a fault diagnosis method based on Auto-Associative Kernel Regression, Fuzzy Similarity and Elman Recurrent NN. This combined method can overcome the limitations of these three independent methods. Considering that the standard Elman-NN can only identify the first-order dynamic model, as the GEARBOX FAULT DIAGNOSIS METHOD BASED ON THE FUSION OF EEMD AND IMPROVED ELMAN-NN. Based on the advantages of the EEMD method and the shortcomings of the standard Elman-NN, this paper puts forward a gearbox fault diagnosis means based on the fusion of EEMD and the improved Elman-NN
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