Health indicator construction for degradation assessment by embedded LSTM–CNN autoencoder and growing self-organized map

Health indicator (HI) construction is the most significant task of degradation assessment (DA) that facilitates prognostic and health management of rotating machinery. Many stacked autoencoder (SAE) models represented by CNN-based and RNN-based SAE have been applied to the field of DA. However, the...

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Vydáno v:	Knowledge-based systems Ročník 252; s. 109399
Hlavní autoři:	Chen, Zhipeng, Zhu, Haiping, Wu, Jun, Fan, Liangzhi
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 27.09.2022
Témata:	Autoencoder Deep learning Degradation assessment Growing self-organized map Transfer learning Deep learning Growing self-organized map Autoencoder Transfer learning Degradation assessment
ISSN:	0950-7051, 1872-7409
On-line přístup:	Získat plný text
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Health indicator construction for degradation assessment by embedded LSTM–CNN​ autoencoder and growing self-organized map

Health indicator construction for degradation assessment by embedded LSTM–CNN autoencoder and growing self-organized map