Semi-Qualitative Trend Analysis for the Monitoring of Process Control Loops

Yoshiyuki Yamashita

doi:10.20965/jaciii.2012.p0503

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JACIII Vol.16 No.4 pp. 503-507

doi: 10.20965/jaciii.2012.p0503

(2012)

Paper:

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Semi-Qualitative Trend Analysis for the Monitoring of Process Control Loops

Yoshiyuki Yamashita

Department of Chemical Engineering, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo 184-8588, Japan

Received:

December 20, 2011

Accepted:

March 31, 2012

Published:

June 20, 2012

Keywords:

process monitoring, process control, fault diagnosis, control valve, maintenance

Abstract

This paper first introduces a semi-qualitative methodology to analyze a time trend based on a qualitative description with the addition of quantitative attributes. Using this methodology, it further defines a method to diagnose and qualify a control valve problem in a process control loop. Finally, the defined method of diagnosis successfully applied to several actual data sets from industrial chemical plants.

Cite this article as:

Y. Yamashita, “Semi-Qualitative Trend Analysis for the Monitoring of Process Control Loops,” J. Adv. Comput. Intell. Intell. Inform., Vol.16 No.4, pp. 503-507, 2012.

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References

[1] S. Dash, M. R. Maurya, and V. Venkatasubramanian, “A novel interval-halving framework for automated identification of process trends,” AIChEJ., Vol.50, No.1, pp. 149-162, 2004.
[2] S. Carbonnier C. Garcia-Beltan, C. Cadet, and S. Gentil, “Trends extraction and analysis for complex system monitoring and decision support,” Engineering Applications of Artificial Intelligence, Vol.18, No.1, pp. 21-36, 2005.
[3] Y. Yamashita, “On-line extraction of qualitative movements for monitoring process plants,” Lecture Note in Artificial Intelligence, Vol.4252, pp. 595-602, 2006.
[4] Y. Yamashita, “An Automated Method for Detection of Stiction in Process Control Loops,” Control Engineering Practice, Vol.14, No.5, pp. 503-510, 2006.
[5] J. T. Cheung and G. Stephanopoulous, “Representation of process trends – Part I: A formal representation framework,” Computers and Chemical Engineering, Vol.14, No.4-5, pp. 495-510, 1990.
[6] B. R. Bakshi and G. Stephanopoulous, “Representation of process trends – Part III: Multi-scale extraction of trends from process data,” Computers and Chemical Engineering, Vol.18, No.4, pp. 267-302, 1994.
[7] D. B. Ender, “Process control performances. Not as good as you think,” Control Engineering, Vol.40, No.1, pp. 180-190, 1993.
[8] T. Hägglund, “A friction compensator for pneumatic control valves,” J. of Process Control, Vol.12, No.8, pp. 897-904, 2002.
[9] R. Srinivasan and R. Rengasawamy, “Approaches for efficient stiction compensation in process control valves,” Computers and Chemical Engineering, Vol.32, No.1-2, pp. 218-229, 2008.
[10] N. F. Thornhill and T. Hägglund, “Detection and Diagnosis of Oscillation in Control Loops,” Int. J. Adaptive Control and Signal Processing, Vol.17, No.7-9, pp. 625-634, 1997.
[11] A. Horch, “A Simple Method for Detection of Sluggish Control Loops,” Control Engineering Practice, Vol.7, No.10, pp. 1221-1231, 1999.
[12] R. Rengaswamy and V. Venkatasubramanian, “A syntactic pattern recognition for process monitoring and fault diagnosis,” Engineering Application of Artificial Intelligence, Vol.8, No.1, pp. 35-51, 1995.
[13] R. Rengaswamy, T. Hägglund, and V. Venkatsubramanian, “A qualitative shape analysis formalism for monitoring control loop performance,” Engineering Applications of Artificial Intelligence, Vol.14, No.1, pp. 23-33, 2001.
[14] M. Kano, H. Maruta, H. Kugemoto, and K. Shimizu, “Practical model and detection algorithm for valve stiction,” Proc. IFAC DYCOPS, Cambridge, USA, 2004.
[15] T. Hägglund, “A shape-analysis approach for diagnosis of stiction in control valves,” Control Engineering Practice, Vol.19, No.8, pp. 782-789, 2011.
[16] D. Kimura, M. Nii, T. Yamaguchi, Y. Takahashi, and T. Yumoto, “Fuzzy Nonlinear Regression Analysis using Fuzzified Neural Networks for Fault Diagnosis of Chemical Plants,” J. Advanced Computational Intelligence and Intelligent Informatics, Vol.15, No.3, pp. 336-344, 2011.
[17] N. F. Thornhill and A. Horch, “Advances and new directions in plant-wide disturbance detection and diagnosis,” Control Engineering Practice, Vol.15, No.10, pp. 1196-1206, 2007.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] S. Dash, M. R. Maurya, and V. Venkatasubramanian, “A novel interval-halving framework for automated identification of process trends,” AIChEJ., Vol.50, No.1, pp. 149-162, 2004.

[2] [2] S. Carbonnier C. Garcia-Beltan, C. Cadet, and S. Gentil, “Trends extraction and analysis for complex system monitoring and decision support,” Engineering Applications of Artificial Intelligence, Vol.18, No.1, pp. 21-36, 2005.

[3] [3] Y. Yamashita, “On-line extraction of qualitative movements for monitoring process plants,” Lecture Note in Artificial Intelligence, Vol.4252, pp. 595-602, 2006.

[4] [4] Y. Yamashita, “An Automated Method for Detection of Stiction in Process Control Loops,” Control Engineering Practice, Vol.14, No.5, pp. 503-510, 2006.

[5] [5] J. T. Cheung and G. Stephanopoulous, “Representation of process trends – Part I: A formal representation framework,” Computers and Chemical Engineering, Vol.14, No.4-5, pp. 495-510, 1990.

[6] [6] B. R. Bakshi and G. Stephanopoulous, “Representation of process trends – Part III: Multi-scale extraction of trends from process data,” Computers and Chemical Engineering, Vol.18, No.4, pp. 267-302, 1994.

[7] [7] D. B. Ender, “Process control performances. Not as good as you think,” Control Engineering, Vol.40, No.1, pp. 180-190, 1993.

[8] [8] T. Hägglund, “A friction compensator for pneumatic control valves,” J. of Process Control, Vol.12, No.8, pp. 897-904, 2002.

[9] [9] R. Srinivasan and R. Rengasawamy, “Approaches for efficient stiction compensation in process control valves,” Computers and Chemical Engineering, Vol.32, No.1-2, pp. 218-229, 2008.

[10] [10] N. F. Thornhill and T. Hägglund, “Detection and Diagnosis of Oscillation in Control Loops,” Int. J. Adaptive Control and Signal Processing, Vol.17, No.7-9, pp. 625-634, 1997.

[11] [11] A. Horch, “A Simple Method for Detection of Sluggish Control Loops,” Control Engineering Practice, Vol.7, No.10, pp. 1221-1231, 1999.

[12] [12] R. Rengaswamy and V. Venkatasubramanian, “A syntactic pattern recognition for process monitoring and fault diagnosis,” Engineering Application of Artificial Intelligence, Vol.8, No.1, pp. 35-51, 1995.

[13] [13] R. Rengaswamy, T. Hägglund, and V. Venkatsubramanian, “A qualitative shape analysis formalism for monitoring control loop performance,” Engineering Applications of Artificial Intelligence, Vol.14, No.1, pp. 23-33, 2001.

[14] [14] M. Kano, H. Maruta, H. Kugemoto, and K. Shimizu, “Practical model and detection algorithm for valve stiction,” Proc. IFAC DYCOPS, Cambridge, USA, 2004.

[15] [15] T. Hägglund, “A shape-analysis approach for diagnosis of stiction in control valves,” Control Engineering Practice, Vol.19, No.8, pp. 782-789, 2011.

[16] [16] D. Kimura, M. Nii, T. Yamaguchi, Y. Takahashi, and T. Yumoto, “Fuzzy Nonlinear Regression Analysis using Fuzzified Neural Networks for Fault Diagnosis of Chemical Plants,” J. Advanced Computational Intelligence and Intelligent Informatics, Vol.15, No.3, pp. 336-344, 2011.

[17] [17] N. F. Thornhill and A. Horch, “Advances and new directions in plant-wide disturbance detection and diagnosis,” Control Engineering Practice, Vol.15, No.10, pp. 1196-1206, 2007.