Research on the Fault Feature Extraction of Rolling Bearings Based on SGMD-CS and the AdaBoost Framework

Research on the Fault Feature Extraction of Rolling Bearings Based on SGMD-CS and the AdaBoost Framework

Symplectic geometric mode decomposition (SGMD) is a newly proposed signal processing method. Because of its superiority, it has gained more and more attention in the field of fault diagnosis. However, the similar component reorganization problem involved in this method has not been clearly stated. A...

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Bibliographic Details
Main Authors: Hui Li, Fan Li, Rong Jia, Fang Zhai, Liang Bai, Xingqi Luo
Format: Article
Language:English
Published: MDPI AG 2021-03-01
Series:Energies
Subjects:
rolling bearings
symplectic geometric mode decomposition
cosine similarity
symplectic geometric entropy
AdaBoost
Online Access:https://www.mdpi.com/1996-1073/14/6/1555

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https://www.mdpi.com/1996-1073/14/6/1555

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