Data Visualization and Visualization-Based Fault Detection for Chemical Processes

Data Visualization and Visualization-Based Fault Detection for Chemical Processes

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...

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Bibliographic Details
Main Authors: Ray C. Wang, Michael Baldea, Thomas F. Edgar
Format: Article
Language:English
Published: MDPI AG 2017-08-01
Series:Processes
Subjects:
data visualization
time series data
multivariate fault detection
Online Access:https://www.mdpi.com/2227-9717/5/3/45
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spelling doaj-1b32136784414514b5664c23925f11742020-11-25T01:30:55ZengMDPI AGProcesses2227-97172017-08-01534510.3390/pr5030045pr5030045Data Visualization and Visualization-Based Fault Detection for Chemical ProcessesRay C. Wang0Michael Baldea1Thomas F. Edgar2McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX 78712, USAMcKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX 78712, USAMcKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX 78712, USAOver 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.https://www.mdpi.com/2227-9717/5/3/45data visualizationtime series datamultivariate fault detection
collection DOAJ
language English
format Article
sources DOAJ
author Ray C. Wang
Michael Baldea
Thomas F. Edgar
spellingShingle Ray C. Wang
Michael Baldea
Thomas F. Edgar
Data Visualization and Visualization-Based Fault Detection for Chemical Processes
Processes
data visualization
time series data
multivariate fault detection
author_facet Ray C. Wang
Michael Baldea
Thomas F. Edgar
author_sort Ray C. Wang
title Data Visualization and Visualization-Based Fault Detection for Chemical Processes
title_short Data Visualization and Visualization-Based Fault Detection for Chemical Processes
title_full Data Visualization and Visualization-Based Fault Detection for Chemical Processes
title_fullStr Data Visualization and Visualization-Based Fault Detection for Chemical Processes
title_full_unstemmed Data Visualization and Visualization-Based Fault Detection for Chemical Processes
title_sort data visualization and visualization-based fault detection for chemical processes
publisher MDPI AG
series Processes
issn 2227-9717
publishDate 2017-08-01
description 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.
topic data visualization
time series data
multivariate fault detection
url https://www.mdpi.com/2227-9717/5/3/45
work_keys_str_mv AT raycwang datavisualizationandvisualizationbasedfaultdetectionforchemicalprocesses
AT michaelbaldea datavisualizationandvisualizationbasedfaultdetectionforchemicalprocesses
AT thomasfedgar datavisualizationandvisualizationbasedfaultdetectionforchemicalprocesses
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