Research on the Fault Diagnosis Method for Rolling Bearings Based on Improved VMD and Automatic IMF Acquisition

Ying Zhang,Anchen Wang

doi:10.1155/2020/6216903

Abstract

This paper proposes a novel method to improve the variational mode decomposition (VMD) method and to automatically acquire the sensitive intrinsic mode function (IMF). First, since fault signals are impulsive and periodic, a weighted autocorrelative function maximum (AFM) indicator is constructed based on the Gini index and autocorrelation function to serve as the optimization objective function. The mode number K and the penalty parameter α of VMD are automatically obtained through an optimal parameter searching process underpinned by the improved particle swarm optimization (PSO) algorithm with a variety of inertia weights. This improvement solves one of the major drawbacks of the conventional VMD method, that is, the need to manually set parameters. Then, an optimal IMF automatic selecting process is performed for single-failure faults and compound faults, according to the principles of the maximum weighted AFM indicator and maximum spectrum peak ratio (SPR), respectively. The sensitive IMFs are then subjected to an envelope demodulation analysis to obtain the fault characteristic frequency. The results of simulations and experiments show that the proposed method can effectively identify fault characteristics early, especially compound faults, demonstrating great potential for real-world applications.

Highlights

Developing techniques for monitoring rolling bearing conditions and diagnosing faults are key to realizing the transition from traditional regular maintenance to condition-based maintenance
To eliminate the need to set parameters in advance, which is a drawback of the variational mode decomposition (VMD), this paper proposes a method for improving the VMD by enabling adaptive parameter acquisition. e method includes three aspects: first, the weighted autocorrelative function maximum (AFM) indicator is constructed as the objective function; second, the improved particle swarm optimization (PSO) is used to search for the optimal parameters of the VMD algorithm and mutual information of the decomposed components, and the original signal is used to detect overdecomposition, and to decide whether or not to perform the search again; third, an optimal intrinsic mode function (IMF) automatic selecting process is performed for the single-failure fault and compound faults following the principles of the maximum weighted AFM and maximum spectrum peak ratio (SPR), respectively
In order to verify the advantages of WAFM index proposed in this paper, the same optimal combination [10, 3146] is used for VMD, and sensitive IMF is selected through the maximum kurtosis principle

Summary

Introduction

Developing techniques for monitoring rolling bearing conditions and diagnosing faults are key to realizing the transition from traditional regular maintenance to condition-based maintenance. Based on the above studies, it can be concluded that constructing a suitable objective function and selecting an appropriate optimization algorithm are two critical issues in the adaptive acquisition of VMD parameters Some indicators such as kurtosis [10,11,12,13,14], correlation coefficient [15], autoregressive (AR) model parameters [16], and entropy [8] have proven extremely useful in vibration signal analysis and fault characteristics extraction. Signal decomposition is realized by iteratively searching for the optimal solution of the variational model in the variational problem framework. e approach achieves good noise robustness and effectively avoids the endpoint effect and modal aliasing problem

WAFM-IPSO-VMD

Simulation Signal

Experimental Signal Analysis

Conclusions