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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Bibliographic Details
Published in:IEEE transactions on industrial informatics Vol. 16; no. 4; pp. 2868 - 2877
Main Authors: Ko, Taeyoung, Kim, Heeyoung
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.04.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2019.2941486