LAPSE:2018.0250
Published Article
LAPSE:2018.0250
Data Visualization and Visualization-Based Fault Detection for Chemical Processes
July 31, 2018
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.
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Keywords
data visualization, multivariate fault detection, time series data
Suggested Citation
Wang RC, Baldea M, Edgar TF. Data Visualization and Visualization-Based Fault Detection for Chemical Processes. (2018). LAPSE:2018.0250
Author Affiliations
Wang RC: McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
Baldea M: McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX 78712, USA; Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
Edgar TF: McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX 78712, USA; Energy Institute, The University of Texas at Austin, Austin, TX 78712, USA
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Baldea M: McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX 78712, USA; Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
Edgar TF: McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX 78712, USA; Energy Institute, The University of Texas at Austin, Austin, TX 78712, USA
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Journal Name
Processes
Volume
5
Issue
3
Article Number
E45
Year
2017
Publication Date
2017-08-14
Published Version
ISSN
2227-9717
Version Comments
Original Submission
Other Meta
PII: pr5030045, Publication Type: Journal Article
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LAPSE:2018.0250
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doi:10.3390/pr5030045
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Jul 31, 2018
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