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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Vydáno v:	Mechanical systems and signal processing Ročník 142; s. 106752
Hlavní autoři:	Li, Xingqiu, Jiang, Hongkai, Niu, Maogui, Wang, Ruixin
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin Elsevier Ltd 01.08.2020 Elsevier BV
Témata:	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
On-line přístup:	Získat plný text
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