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Processes 2017, 5(3), 45; doi:10.3390/pr5030045

Data Visualization and Visualization-Based Fault Detection for Chemical Processes

1,†,‡
,
1,2,†,‡,* and 1,3,†,‡
1
McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
2
Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
3
Energy Institute, The University of Texas at Austin, Austin, TX 78712, USA
Current address: 200 E Dean Keeton St. Austin, TX 78712, USA.
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 2 June 2017 / Revised: 18 July 2017 / Accepted: 31 July 2017 / Published: 14 August 2017
(This article belongs to the Collection Process Data Analytics)
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Abstract

Over the years, there has been a consistent increase in the amount of data collected by systems and processes in many different industries and fields. Simultaneously, there is a growing push towards revealing and exploiting of the information contained therein. The chemical processes industry is one such field, with high volume and high-dimensional time series data. In this paper, we present a unified overview of the application of recently-developed data visualization concepts to fault detection in the chemical industry. We consider three common types of processes and compare visualization-based fault detection performance to methods used currently. View Full-Text
Keywords: data visualization; time series data; multivariate fault detection data visualization; time series data; multivariate fault detection
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Wang, R.C.; Baldea, M.; Edgar, T.F. Data Visualization and Visualization-Based Fault Detection for Chemical Processes. Processes 2017, 5, 45.

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