2023
DOI: 10.3390/e25071049
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Use of Composite Multivariate Multiscale Permutation Fuzzy Entropy to Diagnose the Faults of Rolling Bearing

Abstract: The study focuses on the fault signals of rolling bearings, which are characterized by nonlinearity, periodic impact, and low signal-to-noise ratio. The advantages of entropy calculation in analyzing time series data were combined with the high calculation accuracy of Multiscale Fuzzy Entropy (MFE) and the strong noise resistance of Multiscale Permutation Entropy (MPE), a multivariate coarse-grained form was introduced, and the coarse-grained process was improved. The Composite Multivariate Multiscale Permutat… Show more

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Cited by 5 publications
(1 citation statement)
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“…To overcome this problem, multiscale entropy (MSE) was proposed by Costa [28] to reveal the inherent complexity of time series over different scales. Subsequently, multiscale PE (MPE), multiscale FE (MFE), and multiscale GE (MGE), etc are proposed by related scholars and applied to rolling bearing feature extraction [29,30]. However, MSE also has some shortcomings, that is, as the scale factor increases, the length of coarse-grained time series will decrease, which leads to noteworthy fluctuations in the estimated MSE curve under larger scale factors, and even information loss and aliasing [20].…”
Section: Introductionmentioning
confidence: 99%
“…To overcome this problem, multiscale entropy (MSE) was proposed by Costa [28] to reveal the inherent complexity of time series over different scales. Subsequently, multiscale PE (MPE), multiscale FE (MFE), and multiscale GE (MGE), etc are proposed by related scholars and applied to rolling bearing feature extraction [29,30]. However, MSE also has some shortcomings, that is, as the scale factor increases, the length of coarse-grained time series will decrease, which leads to noteworthy fluctuations in the estimated MSE curve under larger scale factors, and even information loss and aliasing [20].…”
Section: Introductionmentioning
confidence: 99%