Research on Fault Feature Extraction Method of Rolling Bearing Based on NMD and Wavelet Threshold Denoising

Abstract

Rolling bearings are the core components of the machine. In order to save costs and prevent accidents caused by bearing failures, the rolling bearing fault diagnosis technology has been widely used in the industrial field. At present, the proposed methods include wavelet transform, morphological filtering, empirical mode decomposition (EMD), and ensemble empirical mode decomposition (EEMD), which have obvious shortcomings. As it is difficult to extract the fault characteristic frequency caused by nonlinear and nonstationary features of the rolling bearing fault signal, this paper presents a fault feature extraction method of rolling bearing based on nonlinear mode decomposition (NMD) and wavelet threshold denoised method. First of all, the fault signal was preprocessed via wavelet threshold denoising. Then, the denoised signal was decomposed by using NMD. Next, the mode component envelope spectrum was made. Finally, the fault characteristic frequency of rolling bearing was extracted. The method was compared with EMD through the simulation experiment and rolling bearing fault experiment. Meanwhile, two indicators including signal-noise ratio (SNR) and root-mean-square error (RMSE) were also established to evaluate the fault diagnosis ability of this method, and the results show that this method can extract the fault characteristic frequency accurately.

An Effective Bearing Fault Diagnosis Technique via Local Robust Principal Component Analysis and Multi-Scale Permutation Entropy

“…It can be seen that the fault feature is completely submerged by noise and the other interferences, which inevitably increases the difficulty of recognizing the fault feature. The methods of wavelet shrinkage denoising [ 14 ], basis pursuit denoising [ 53 ], EMD, and SSA were selected for comparative analysis.…”

“…Deng et al [ 13 ] presented a novel fault diagnosis method for a motor bearing based on integrating empirical wavelet transform (IEWT) and fuzzy entropy. Xiao et al [ 14 ] applied the wavelet threshold denoising method to effectively de-noise a rolling bearing signal. However, the diagnostic performance of these methods depends on the selection of the wavelet basis functions and the threshold.…”

Noise Reduction Method of Underwater Acoustic Signals Based on CEEMDAN, Effort-To-Compress Complexity, Refined Composite Multiscale Dispersion Entropy and Wavelet Threshold Denoising

“…Wavelet analysis developed from Fourier analysis is a new time-frequency analysis tool, which has favorable time-frequency localized and multi-resolution properties. Wavelet analysis has been widely applied in signal processing field [ 38 , 39 , 40 ]. The specific steps of wavelet transform are as follows:…”

The Fault Diagnosis of Rolling Bearing Based on Improved Deep Forest

“…Vibration signals of rolling bearing fault are usually nonstationary and nonlinear [3,4]. e early bearing fault identification technologies are mainly based on time domain, frequency domain, and time-frequency signal analysis methods [5][6][7].…”