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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Veröffentlicht in:	Knowledge-based systems Jg. 252; S. 109399
Hauptverfasser:	Chen, Zhipeng, Zhu, Haiping, Wu, Jun, Fan, Liangzhi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 27.09.2022
Schlagworte:	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
Online-Zugang:	Volltext
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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