A novel fault early warning method for centrifugal blowers based on stacked denoising autoencoder and transfer learning

Centrifugal blowers are easy to get faults due to the harsh working environment, and appropriate fault early warning is of great significance for predictive maintenance. Traditional fault early warning methods have poor resistance and feature learning ability in dealing with multivariate data with n...

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Vydané v:	Journal of manufacturing systems Ročník 76; s. 443 - 456
Hlavní autori:	Zhang, You, Li, Congbo, Tang, Ying, Zhang, Xu, Zhou, Feng
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.10.2024
Predmet:	Centrifugal blowers Fault early warning Stacked denoising autoencoder Transfer learning Centrifugal blowers Stacked denoising autoencoder Transfer learning Fault early warning
ISSN:	0278-6125
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