Process Systems Engineering

Process monitoring using principal components in parallel coordinates

  1. Ricardo Dunia1,*,
  2. Thomas F. Edgar1,
  3. Mark Nixon2

Article first published online: 5 JUN 2012

DOI: 10.1002/aic.13846

Issue

AIChE Journal

AIChE Journal

Volume 59, Issue 2, pages 445–456, February 2013

Additional Information

How to Cite

Dunia, R., Edgar, T. F. and Nixon, M. (2013), Process monitoring using principal components in parallel coordinates. AIChE J., 59: 445–456. doi: 10.1002/aic.13846

Author Information

  1. 1

    Dept. of Chemical Engineering, The University of Texas at Austin, Austin, TX

  2. 2

    Emerson Process Management, Austin, TX

*Correspondence concerning this article should be addressed to R. Dunia at rdunia@che.utexas.edu.

Publication History

  1. Issue published online: 23 JAN 2013
  2. Article first published online: 5 JUN 2012
  3. Accepted manuscript online: 18 MAY 2012 09:57AM EST
  4. Manuscript Revised: 13 APR 2012
  5. Manuscript Received: 8 FEB 2012

Funded by

  • Emerson Process Management
  • Center for Operator Performance (COP)

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Keywords:

  • process monitoring;
  • parallel coordinates;
  • principal component analysis;
  • fault detection;
  • multivariable statistical process control;
  • data visualization;
  • PCA

Parallel coordinates is a recognized visualization technique in which data points, each defined by multiple coordinates, are represented by an unlimited number of adjoining parallel axes. This type of visualization technique is suitable for process monitoring applications in industrial facilities where a significant number of sensors are used to detect and identify abnormal operating conditions. This work makes use of principal component monitoring methods implemented in parallel coordinates plots, named PC2. The PC2 capabilities to visualize confidence regions of operations, evaluate models with different number of principal components, compare faulty events and determine the frequency of false alarms are here demonstrated. The monitoring visualization technology presented by PC2 was successfully used for early detection of compressor surge and column flooding using actual process data. © 2012 American Institute of Chemical Engineers AIChE J, 59: 445–456, 2013

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