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Data Mining of Historic Data for Process Identification
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Perstorp AB, Sweden.
2011 (English)Report (Other academic)
Abstract [en]

Performing experiments for system identication is often a time-consuming task which may also interfere with the process operation. With memory prices going down, it is more and more common that years of process data are stored (without compression) in a history database. The rationale for this work is that in such stored data there must already be intervals informative enough for system identication. Therefore, the goal of this project was to find an algorithm that searches and marks intervals suitable for process identication (rather than performing completely automatic system identication). For each loop, 4 stored variables are required; setpoint, manipulated variable, process output and mode of the controller.

The proposed method requires a minimum of knowledge of the process and is implemented in a simple and ecient recursive algorithm. The essential features of the method are the search for excitation of the input and output, followed by the estimation of a Laguerre model combined with a chi-square test to check that at least one estimated parameter is statistically signicant. The use of Laguerre models is crucial to handle processes with deadtime without explicit delay estimation. The method was tested on three years of data from more than 200 control loops. It was able to find all intervals in which known identication experiments were performed as well as many other useful intervals in closed/open loop operation.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2011. , 9 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 3039
Keyword [en]
Data mining, Data segmentation, System identification, Excitation, Condition numbers, Laguerre filters
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-97980ISRN: LiTH-ISY-R-3039OAI: oai:DiVA.org:liu-97980DiVA: diva2:650878
Funder
Swedish Foundation for Strategic Research
Available from: 2013-09-23 Created: 2013-09-23 Last updated: 2014-09-19Bibliographically approved

Open Access in DiVA

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