The fault diagnosis method of rolling bearing under variable working conditions based on deep transfer learning

The vibration signals of rolling bearing obtained under variable working conditions do not obey the same independent distribution so that the traditional method of bearing fault diagnosis has low accuracy, a fault diagnosis method about rolling bearing based on sparse denoising autoencoder (SDAE) fo...

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Vydané v:	Journal of the Brazilian Society of Mechanical Sciences and Engineering Ročník 42; číslo 11
Hlavní autori:	Dong, Shaojiang, He, Kun, Tang, Baoping
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2020 Springer Nature B.V
Predmet:	Algorithms Engineering Fault diagnosis Feature extraction Fourier transforms Learning Mechanical Engineering Noise reduction Roller bearings Statistical methods Technical Paper Vibration Working conditions Fault diagnosis Variable working conditions Transfer learning Rolling bearing Sparse denoising autoencoder
ISSN:	1678-5878, 1806-3691
On-line prístup:	Získať plný text
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