An Improved Variational Mode Decomposition and Its Application on Fault Feature Extraction of Rolling Element Bearing

Guoping An,Qingbin Tong,Yuyi Lin,Junci Cao,Ruifang Liu,Yanan Zhang,Weili Li

doi:10.3390/en14041079

Abstract

The fault diagnosis of rolling element bearing is of great significance to avoid serious accidents and huge economic losses. However, the characteristics of the nonlinear, non-stationary vibration signals make the fault feature extraction of signal become a challenging work. This paper proposes an improved variational mode decomposition (IVMD) algorithm for the fault feature extraction of rolling bearing, which has the advantages of extracting the optimal fault feature from the decomposed mode and overcoming the noise interference. The Shuffled Frog Leap Algorithm (SFLA) is employed in the optimal adaptive selection of mode number K and bandwidth control parameter α. A multi-objective evaluation function, which is based on the envelope entropy, kurtosis and correlation coefficients, is constructed to select the optimal mode component. The efficiency coefficient method (ECM) is utilized to transform the multi-objective optimization problem into a single-objective optimization problem. The envelope spectrum technique is used to analyze the signals reconstructed by the optimal mode components. The proposed IVMD method is evaluated by simulation and practical bearing vibration signals under different conditions. The results show that the proposed method can improve the decomposition accuracy of the signal and the adaptability of the influence parameters and realize the effective extraction of the bearing vibration signal.

Highlights

As the key component of rotating machinery, the rolling bearings play an important role in the rotating machinery of modern industry
The multi-objective evaluation function is constructed for the selection of the optimal mode component and the calculation of fitness value, which is based on the envelope entropy, the kurtosis, and the correlation coefficients
The improved variational mode decomposition (IVMD) in this paper was developed to achieve the accurate decomposition of fault signal and adaptive control of influence parameters

Summary

Introduction

As the key component of rotating machinery, the rolling bearings play an important role in the rotating machinery of modern industry. Due to the application of Wiener filters, the narrow-banded function of VMD resultant modes reduces the mode mixing issues existing in the EMD and helps to accurately extract the fault characteristics of the signal through the Hilbert transform. VMD is a new adaptive and quasi-orthogonal signal processing method [29], through which an input signal f (t) can be decomposed into a limited number of IMFs. By solving the optimal solution of the constrained variational problem, the central frequency and bandlimited of each mode can be decided.

Shuffled Frog Leaping Algorithm

Improved VMD Algorithm

Actual Vibration Signal Analysis

Conclusions