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...

Full description

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

Internet

http://www.mdpi.com/1424-8220/18/7/2325

Similar Items