Abstract
The complex and harsh working environment of rolling bearings cause the fault characteristics in vibration signal contaminated by the noise, which make fault diagnosis difficult. In this paper, a feature enhancement method of rolling bearing signal based on variational mode decomposition with K determined adaptively (K-adaptive VMD), and radial based function fuzzy entropy (RBF-FuzzyEn), is proposed. Firstly, a phenomenon called abnormal decline of center frequency (ADCF) is defined in order to determine the parameter K of VMD adaptively. Then, the raw signal is separated into K intrinsic mode functions (IMFs). A coefficient En for selecting optimal IMFs is calculated based on the center frequency bands (CFBs) of all IMFs and frequency spectrum for original signal autocorrelation operation. After that, the optimal IMFs of which En are bigger than the threshold are selected to reconstruct signal. Secondly, RBF is introduced as an innovative fuzzy function to enhance the feature discrimination of fuzzy entropy between bearings in different states. A specific way for determination of parameter r in fuzzy function is also presented. Finally, RBF-FuzzyEn is used to extract features of reconstructed signal. Simulation and experiment results show that K-adaptive VMD can effectively reduce the noise and enhance the fault characteristics; RBF-FuzzyEn has strong feature differentiation, superior noise robustness, and low dependence on data length.
Highlights
Rolling bearing is one of the most widely used components in mechanical equipment.The working environment of bearing is relatively bad, and the failure rate is high [1]
variational mode decomposition (VMD) and fuzzy entropy are improved from aspects of noise reduction and feature extraction respectively, forming a feature enhancement method that combines the K-adaptive VMD and Radial based function (RBF)-FuzzyEn for the fault diagnosis of bearing
An algorithm of K-adaptive VMD was developed, which can obtain K adaptively based on abnormal decline of center frequency (ADCF), and select the optimal intrinsic mode functions (IMFs) by a coefficient En
Summary
Rolling bearing is one of the most widely used components in mechanical equipment. The working environment of bearing is relatively bad, and the failure rate is high [1]. There remain two main problems to be solved in realizing noise reduction of rolling bearing vibration data by VMD: determine the number of modes K and penalty factor α; select IMFs and reconstruct the signal. We proposed K-adaptive VMD, which does not set an upper limit for K, and adaptively determines K according to the abnormal decline of center frequency disorder (ADCF) of IMFs. Aiming at problem two, most of the researches are based on statistical indicators of signals in time or frequency domain, such as kurtosis, permutation entropy, envelope spectral kurtosis, correlation coefficient, Euclidean distance and mutual information, etc. The specific implementation steps of the rolling bearing vibration signal feature enhancement method based on K-adaptive VMD and RBF-FuzzyEn proposed in this paper are listed hereafter.
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