Robust Monitoring and Fault Isolation of Nonlinear Industrial Processes Using Denoising Autoencoder and Elastic Net

Robust process monitoring and reliable fault isolation in industrial processes usually encounter different challenges, including process nonlinearity and noise interference. In this brief, a novel method denoising autoencoder and elastic net (DAE-EN) is proposed to solve the aforementioned issues by...

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Veröffentlicht in:	IEEE transactions on control systems technology Jg. 28; H. 3; S. 1083 - 1091
Hauptverfasser:	Yu, Wanke, Zhao, Chunhui
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.05.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Coders Data mining Denoising autoencoder (DAE) Discriminant analysis elastic net (EN) fault isolation Feature extraction Kernel kernel density estimation (KDE) Monitoring Neurons Noise reduction Nonlinearity Principal component analysis process monitoring Robustness Statistical methods
ISSN:	1063-6536, 1558-0865
Online-Zugang:	Volltext
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