Parameter-Adaptive VMD Method Based on BAS Optimization Algorithm for Incipient Bearing Fault Diagnosis

Heng-Di Wang,Jian-Xi Yang,Si-Er Deng,Hui Liao,Wen-Bo Li

doi:10.1155/2020/5659618

Abstract

In view of the incipient fault characteristics are difficult to be extracted from the raw bearing fault signals, an incipient bearing fault diagnosis method based on parameter-adaptive variational mode decomposition (VMD) is proposed. The beetle antennae search (BAS) algorithm is adopted to seek for the optimal combination of the VMD parameters. The reciprocals of the calculated kurtosis values of intrinsic mode functions (IMFs) decomposed via VMD are employed as a fitness function in the searching process. The optimal mode number and the quadratic penalty term of VMD are adaptively set after the search. Afterwards, a vibration signal is decomposed into a set of IMFs using the parameter-adaptive VMD, and the IMF with the maximal kurtosis value is selected as the sensitive one. The selected IMF is further analyzed by Hilbert envelope demodulation. The resulting envelope spectrum can show the significant fault impulse characteristics which are highly helpful to diagnose incipient bearing faults. The kurtosis and the proportion of fault energy are introduced as the input vector of the extreme learning machine (ELM). Comparisons have been conducted via ELM to evaluate the performance by using EMD and the fixed-parameter VMD. The experimental results demonstrate that the proposed method is more effective in extracting the incipient bearing fault characteristics.

Highlights

Bearing is one of the most critical components in rotating machinery. e presence of defects in the bearing may lead to noise, vibration, or even system breakdown
Seeking for the optimum of M and α that matches with the analyzed signals is the key to the variational mode decomposition (VMD) method
Conclusions e VMD method can completely decompose the raw vibration signals into a set of intrinsic mode functions (IMFs) from low frequency to high frequency. e mode number M and the quadratic penalty term α of VMD will have considerable influence on decomposition results. e key to the VMD method lies in seeking for the optimal combination of M and α that matches with the analyzed signals

Summary

Introduction

Bearing is one of the most critical components in rotating machinery. e presence of defects in the bearing may lead to noise, vibration, or even system breakdown. A parameter-adaptive VMD method based on BAS for incipient bearing fault diagnosis is introduced. The kurtosis is effective for the diagnosis of bearing faults and can be used to identify the conditions of the bearings In consideration of this advantage, the reciprocal of kurtosis values calculated from modes may be adopted as the fitness function to optimize VMD parameters. After obtaining the optimal parameters of VMD, the vibration signals are decomposed into a set of IMFs via VMD, and the mode with the maximal kurtosis is selected as the sensitive one. Step 8: decompose the vibration signals into a set of IMF components via VMD by using the optimal M and α, and choose the IMF with the maximal kurtosis. Decomposition results of the signal via VMD with the optimal parameters are presented in Figure 4. e kurtosis values (K) of IMFs are calculated and listed in Table 2. e maximal kurtosis appears in IMF3, namely, the IMF3 has the most observable impact composition

Fault characteristic frequencies

Mode K

Findings

Testing accuracy