The enhancement of fault detection for rolling bearing via optimized VMD and TQWT based sparse code shrinkage

Xing Yuan,Hui Liu,Huijie Zhang

doi:10.21595/jve.2021.22201

Abstract

A typical vibration signal of fault bearing is composed of periodic repetitive transient impulses, multiple vibration disturbance and background noise. Variational mode decomposition (VMD) represents a potential tool for analyzing such signals. However, the reasonable selection of VMD algorithm parameters hinders its application in mechanical signal processing to a certain extent. According to the specific characteristics of rolling bearing fault signal, the composite dimensionless index is constructed as the objective function to ensure the optimal decomposition of VMD. To further enhance the fault characteristics, the tunable Q-factor wavelet transform (TQWT) along with sparse code shrinkage is proposed to denoise the modal components containing periodic impulses, which further highlights the impulses and improves the sparseness of fault signal. Simulation and experimental signal analysis verify the effectiveness and reliability of this method. The results show that the use of optimized VMD and TQWT based sparse code shrinkage dramatically sharpens the impulses from the mixed signal with noise interference and increases the sparseness to a level.

Highlights

Rolling bearing is the core of almost every rotating machine
The impulses, which are contained in the optimal component of the optimized Variational mode decomposition (VMD), are enhanced using the tunable Q-factor wavelet transform (TQWT) based sparse code shrinkage method, which effectively matches the specific damped oscillation behavior, clarifies the impulses and spares representation fault signal
It can be seen that the comprehensive use of optimized VMD and TQWT based sparse code shrinkage has good reliability and feasibility in sparse enhancement and weak impulse feature extraction

Summary

Introduction

Rolling bearing is the core of almost every rotating machine. As a common fault source, they have been widely concerned in the field of vibration analysis. THE ENHANCEMENT OF FAULT DETECTION FOR ROLLING BEARING VIA OPTIMIZED VMD AND TQWT BASED SPARSE CODE SHRINKAGE. A new TQWT based sparse code shrinkage method is proposed, which can well match the damped oscillation mode of bearing fault signal and enhance the transient impulse characteristics. The impulses, which are contained in the optimal component of the optimized VMD, are enhanced using the TQWT based sparse code shrinkage method, which effectively matches the specific damped oscillation behavior, clarifies the impulses and spares representation fault signal.

Theoretical basis of VMD

Parameter optimization method

Tunable Q-factor wavelet transform

Sparse code shrinkage

Adaptive sparse representation

Simulation experiment

Experimental verification

Findings

Conclusions