Fault Classification in High-Dimensional Complex Processes Using Semi-Supervised Deep Convolutional Generative Models

In complex industrial processes, process fault detection and classification constitute an important task for reducing production costs and improving product quality. Most existing methods for fault classification assume that sufficient labeled data are available for training. However, label acquisit...

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Vydané v:	IEEE transactions on industrial informatics Ročník 16; číslo 4; s. 2868 - 2877
Hlavní autori:	Ko, Taeyoung, Kim, Heeyoung
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.04.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Artificial neural networks Classification Convolutional auxiliary deep generative model Correlation Data models Data processing Fault detection Feature extraction Multivariate analysis multivariate time series Production costs semi-supervised convolutional variational autoencoder Sensors Task analysis Tennessee Eastman process Time series analysis unlabeled data
ISSN:	1551-3203, 1941-0050
On-line prístup:	Získať plný text
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