Abstract
In the fault monitoring of rotating machinery, the vibration signal of the bearing and gear in a complex operating environment has poor stationarity and high noise. How to accurately and efficiently identify various fault categories is a major challenge in rotary fault diagnosis. Most of the existing methods only analyze the single channel vibration signal and do not comprehensively consider the multi-channel vibration signal. Therefore, this paper presents Refined Composite Multivariate Multiscale Fluctuation Dispersion Entropy (RCMMFDE), a method which extracts the recognition information of multi-channel signals with different scale factors, and the refined composite analysis ensures the recognition stability. The simulation results show that this method has the characteristics of low sensitivity to signal length and strong anti-noise ability. At the same time, combined with Joint Mutual Information Maximisation (JMIM) and support vector machine (SVM), RCMMFDE-JMIM-SVM fault diagnosis method has been proposed. This method uses RCMMFDE to extract the state characteristics of the multiple vibration signals of the rotary machine, and then uses the JMIM method to extract the sensitive characteristics. Finally, different states of the rotary machine are classified by SVM. The validity of the method is verified by the composite gear fault data set and bearing fault data set. The diagnostic accuracy of the method is 99.25% and 100.00%. The experimental results show that RCMMFDE-JMIM-SVM can effectively recognize multiple signals.
Highlights
Rotating machinery plays an important role in the modern industry with a complex dynamic system
Zhou et al proposed that refined complex multi-scale fluctuation dispersion entropy (RCMFDE) [15] is stronger and more stable in extracting features, and RCMDE has a smaller dependence on the length of time series
In order to solve the shortage of RCMFDE in multivariable time series and the poor stability of multi-variable multi-scale sample entropy (mvMSE) in feature extraction, in this paper, a refined composite multivariate multiscale fluctuation dispersion entropy is proposed
Summary
Rotating machinery plays an important role in the modern industry with a complex dynamic system. In order to solve the shortage of RCMFDE in multivariable time series and the poor stability of mvMSE in feature extraction, in this paper, a refined composite multivariate multiscale fluctuation dispersion entropy is proposed This method synthesizes the information of multiple coarse-grained sequences in each channel of the multi-variable time series, and uses a refine composite method to make it less dependent on the length of the time series and more stable and reliable in feature extraction. A fault diagnosis method based on Refined Composite Multivariate Multiscale Fluctuation Dispersion Entropy, Joint Mutual Information Maximisation, and Support Vector Machine (RCMMFDJMIM-SVM) is proposed In this method, RCMMFDE extracts the multi-variable time-series information of rotating machinery faults, uses JMIM to extract sensitive features, reduce feature dimension, and reduce the time of fault diagnosis. 1 τ τ pτa is the average probability of the dispersion mode πv0v1...vm−2 of coarse-grained sequence Xaτ. pτa is the frequency of scattering mode πv0v1...vm−2 in the A multivariate coarse-grained time series Xaτ
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