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

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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 acquisition is costly and laborious in practice, whereas abundant unlabeled data are often available. To make effective use of a large amount of unlabeled data for fault classification, we propose in this article a new approach using semi-supervised deep generative models, allowing the complex relationship between high-dimensional process data and the process status to be modeled. In particular, to consider the temporal correlation and intervariable correlation in multivariate time series process data collected from multiple sensors, we propose two semi-supervised deep generative models incorporating convolutional neural networks. The proposed models are assessed on data from the Tennessee Eastman benchmark process. The results demonstrate the superior performances of the proposed models compared with competing methods.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2020-04
Language
English
Article Type
Article
Citation

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, v.16, no.4, pp.2868 - 2877

ISSN
1551-3203
DOI
10.1109/TII.2019.2941486
URI
http://hdl.handle.net/10203/272348
Appears in Collection
IE-Journal Papers(저널논문)
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