A qualitative shape analysis formalism for monitoring, control loop performance
(2001) In Engineering Applications of Artificial Intelligence 14(1). p.23-33- Abstract
- Optimal performance of controllers and control loops is crucial for process economy, quality and safety in chemical plants. Industrial statistics show that often a significant percentage of them are performing sub-optimally at any given time. Effective real-time monitoring of control loops is a difficult task as there may be dozens of loops to monitor in a typical process. In addition, abnormal or suboptimal performance is often not apparent under cursory inspection. Hence, automated approaches for the real-time monitoring of control loop performance is of considerable insterest. In this paper, we propose an automated qualitative shape analysis (QSA) formalism for detecting and diagnosing different kinds of oscillations in control loops.... (More)
- Optimal performance of controllers and control loops is crucial for process economy, quality and safety in chemical plants. Industrial statistics show that often a significant percentage of them are performing sub-optimally at any given time. Effective real-time monitoring of control loops is a difficult task as there may be dozens of loops to monitor in a typical process. In addition, abnormal or suboptimal performance is often not apparent under cursory inspection. Hence, automated approaches for the real-time monitoring of control loop performance is of considerable insterest. In this paper, we propose an automated qualitative shape analysis (QSA) formalism for detecting and diagnosing different kinds of oscillations in control loops. We extend our earlier QSA methodology to make it more robust by developing an algorithm for automatic identification of the appropriate global time-scales. We demonstrate this formalism on three case studies to detect and diagnose control loop oscillations. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/162819
- author
- Rengaswamy, R ; Hägglund, Tore LU and Venkatasubramanian, V.
- organization
- publishing date
- 2001
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Engineering Applications of Artificial Intelligence
- volume
- 14
- issue
- 1
- pages
- 23 - 33
- publisher
- Engineering Applications of Artificial Intelligence
- external identifiers
-
- scopus:0342903324
- ISSN
- 1873-6769
- DOI
- 10.1016/S0952-1976(00)00051-8
- language
- English
- LU publication?
- yes
- id
- fc724fba-121d-408b-9214-f76ad28d9adb (old id 162819)
- date added to LUP
- 2016-04-01 12:27:02
- date last changed
- 2022-04-13 19:11:45
@article{fc724fba-121d-408b-9214-f76ad28d9adb,
abstract = {{Optimal performance of controllers and control loops is crucial for process economy, quality and safety in chemical plants. Industrial statistics show that often a significant percentage of them are performing sub-optimally at any given time. Effective real-time monitoring of control loops is a difficult task as there may be dozens of loops to monitor in a typical process. In addition, abnormal or suboptimal performance is often not apparent under cursory inspection. Hence, automated approaches for the real-time monitoring of control loop performance is of considerable insterest. In this paper, we propose an automated qualitative shape analysis (QSA) formalism for detecting and diagnosing different kinds of oscillations in control loops. We extend our earlier QSA methodology to make it more robust by developing an algorithm for automatic identification of the appropriate global time-scales. We demonstrate this formalism on three case studies to detect and diagnose control loop oscillations.}},
author = {{Rengaswamy, R and Hägglund, Tore and Venkatasubramanian, V.}},
issn = {{1873-6769}},
language = {{eng}},
number = {{1}},
pages = {{23--33}},
publisher = {{Engineering Applications of Artificial Intelligence}},
series = {{Engineering Applications of Artificial Intelligence}},
title = {{A qualitative shape analysis formalism for monitoring, control loop performance}},
url = {{http://dx.doi.org/10.1016/S0952-1976(00)00051-8}},
doi = {{10.1016/S0952-1976(00)00051-8}},
volume = {{14}},
year = {{2001}},
}