Automated feature learning for nonlinear process monitoring – An approach using stacked denoising autoencoder and k-nearest neighbor rule

•Automated feature learning based on stacked denoising autoencoder (SDAE) and k-nearest neighbor rule (kNN) for nonlinear process monitoring.•SDAE is used to automatically learn the patterns inherent in the nonlinear process and extract key features.•New monitoring statistics that are HD2 and RD2 ar...

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Veröffentlicht in:	Journal of process control Jg. 64; S. 49 - 61
Hauptverfasser:	Zhang, Zehan, Jiang, Teng, Li, Shuanghong, Yang, Yupu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.04.2018
Schlagworte:	Automated feature learning Deep learning k-Nearest neighbor rule Nonlinear process monitoring Stacked denoising autoencoder Deep learning Nonlinear process monitoring Stacked denoising autoencoder Automated feature learning k-Nearest neighbor rule
ISSN:	0959-1524, 1873-2771
Online-Zugang:	Volltext
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