Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification

Effective fault diagnosis has long been a research topic in the prognosis and health management of rotary machinery engineered systems due to the benefits such as safety guarantees, reliability improvements, and economical efficiency. This paper investigates an effective and reliable deep learning m...

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Veröffentlicht in:	Signal processing Jg. 130; S. 377 - 388
Hauptverfasser:	Lu, Chen, Wang, Zhen-Ya, Qin, Wei-Li, Ma, Jian
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.01.2017
Schlagworte:	Deep learning Fault diagnosis Health state identification Stacked denoising autoencoder Fault diagnosis Deep learning Health state identification Stacked denoising autoencoder
ISSN:	0165-1684, 1872-7557
Online-Zugang:	Volltext
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