A Parameter-Optimized Variational Mode Decomposition Investigation for Fault Feature Extraction of Rolling Element Bearings

Yanan Zhang,Guoping An,Yong Chen,Qingbin Tong,Qiang Wang,Xiaowen Pu,Weili Li,Junci Cao,Ying Zhu,Ruifang Liu,Yuyi Lin

doi:10.1155/2021/6629474

Abstract

Reliable fault diagnosis of the rolling element bearings highly relies on the correct extraction of fault-related features from vibration signals in time-frequency analysis. However, considering the nonlinear, nonstationary characteristics of vibration signals, the extraction of fault features hidden in the heavy noise has become a challenging task. Variable mode decomposition (VMD) is an adaptive, completely nonrecursive method of mode variation and signal processing. This paper analyzes the advantages of VMD compared with EMD in robustness of against noise, overcoming the end effect and mode aliasing. The signal decomposition performance of VMD algorithm largely depends on the selection of mode number k and bandwidth control parameter α. To realize the adaptability of influence parameters and the improvement of decomposition accuracy, a parameter-optimized VMD method is presented. The random frog leaping algorithm (SFLA) is used to search the optimal combination of influence parameters, and the mode number and bandwidth control parameters are set according to the search results. A multiobjective evaluation function is constructed to select the optimal mode component. The envelope spectrum technique is used to analyze the optimal mode component. The proposed 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

Rolling element bearings, as a very important component of rotating machinery, has been widely used in modern industry such as engineering machinery and aerospace [1, 2].e working state of rolling element bearings is directly related to the safety of the rotating machinery
Rolling element bearings are damaged under the long-term operation of harsh environment with high speed, heavy load, strong impact, and high temperature. e developed mechanical faults may cause the deterioration of machine operating conditions, resulting in serious economic losses and casualties [3,4,5]. e vibration signal detected by the sensor is always related to the important physical information that a series of shock pulses will occur when the rolling element bearing is subjected to a local fault [6, 7]
To effectively analyze the fault features from the vibration signals, some traditional time-frequency analysis methods have been widely used, such as short-time Fourier transform (STFT) [10], Wigner–Ville distribution (WVD) [11], and wavelet transform (WT) [12]

Summary

Research Article

A Parameter-Optimized Variational Mode Decomposition Investigation for Fault Feature Extraction of Rolling Element Bearings. Reliable fault diagnosis of the rolling element bearings highly relies on the correct extraction of fault-related features from vibration signals in time-frequency analysis. E signal decomposition performance of VMD algorithm largely depends on the selection of mode number k and bandwidth control parameter α. To realize the adaptability of influence parameters and the improvement of decomposition accuracy, a parameteroptimized VMD method is presented. E random frog leaping algorithm (SFLA) is used to search the optimal combination of influence parameters, and the mode number and bandwidth control parameters are set according to the search results. E 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 A multiobjective evaluation function is constructed to select the optimal mode component. e envelope spectrum technique is used to analyze the optimal mode component. e proposed method is evaluated by simulation and practical bearing vibration signals under different conditions. e 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

Introduction

Simulated signal

Experimental Results and Analysis

Amplitude f