An enhanced selective ensemble deep learning method for rolling bearing fault diagnosis with beetle antennae search algorithm

•Deep base models are constructed to adaptively learn latent features from vibration signals.•Multiple diverse deep base models are acquired by variants of AEs and Bootstrap.•EWV with class-specific thresholds is designed to implement selective ensemble.•BAS algorithm is used to optimize the class-s...

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Bibliographic Details
Published in:	Mechanical systems and signal processing Vol. 142; p. 106752
Main Authors:	Li, Xingqiu, Jiang, Hongkai, Niu, Maogui, Wang, Ruixin
Format:	Journal Article
Language:	English
Published:	Berlin Elsevier Ltd 01.08.2020 Elsevier BV
Subjects:	Algorithms Beetle antennae search algorithm Deep learning Enhanced selective ensemble deep learning Enhanced weighted voting combination strategy Fault diagnosis Machine learning Noise reduction Performance enhancement Roller bearings Rolling bearing Search algorithms Teaching methods Thresholds Fault diagnosis Enhanced selective ensemble deep learning Enhanced weighted voting combination strategy Beetle antennae search algorithm Rolling bearing
ISSN:	0888-3270, 1096-1216
Online Access:	Get full text
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