Trivariate Empirical Mode Decomposition via Convex Optimization for Rolling Bearing Condition Identification

Trivariate Empirical Mode Decomposition via Convex Optimization for Rolling Bearing Condition Identification

As a multichannel signal processing method based on data-driven, multivariate empirical mode decomposition (MEMD) has attracted much attention due to its potential ability in self-adaption and multi-scale decomposition for multivariate data. Commonly, the uniform projection scheme on a hypersphere i...

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Bibliographic Details
Main Authors:	Yong Lv, Houzhuang Zhang, Cancan Yi
Format:	Article
Language:	English
Published:	MDPI AG 2018-07-01
Series:	Sensors
Subjects:	trivariate empirical mode decomposition convex optimization low-rank matrix approximation rolling bearing condition identification
Online Access:	http://www.mdpi.com/1424-8220/18/7/2325

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