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Processes 2015, 3(2), 357-383; doi:10.3390/pr3020357

An Algorithm for Finding Process Identification Intervals from Normal Operating Data

1
Department of Electrical Engineering, Linköping University, SE-581 83 Linköping, Sweden
2
ABB AB, Corporate Resarch, SE-721 78 Västerås, Sweden
3
Hella Fahrzeugkomponenten GmbH, 28199 Bremen, Germany
4
Perstorp AB, SE-284 80 Perstorp, Sweden
This paper is an extended version of our paper published in the Proceedings of the 2011 AIChE Annual Meeting, Minneapolis, MN, USA, 16-21 October 2011.
*
Author to whom correspondence should be addressed.
Academic Editor: Gabriele Pannocchia
Received: 24 January 2015 / Accepted: 23 April 2015 / Published: 6 May 2015
(This article belongs to the Special Issue Process Control: Current Trends and Future Challenges)
View Full-Text   |   Download PDF [396 KB, uploaded 6 May 2015]   |  

Abstract

Performing experiments for system identification is often a time-consuming task which may also interfere with the process operation. With memory prices going down and the possibility of cloud storage, years of data is more and more commonly stored (without compression) in a history database. In such stored data, there may already be intervals informative enough for system identification. Therefore, the goal of this project was to find an algorithm that searches and marks intervals suitable for process identification (rather than completely autonomous system identification). For each loop, four stored variables are required: setpoint, manipulated variable, measured process output and mode of the controller. 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 hypothesis test to check that there is a causal relationship between process input and output. 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 about 200 control loops. It was able to find all intervals in which known identification experiments were performed as well as many other useful intervals in closed/open loop operation. View Full-Text
Keywords: data mining; system identification; process control; excitation; condition number; Laguerre filter data mining; system identification; process control; excitation; condition number; Laguerre filter
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Bittencourt, A.C.; Isaksson, A.J.; Peretzki, D.; Forsman, K. An Algorithm for Finding Process Identification Intervals from Normal Operating Data. Processes 2015, 3, 357-383.

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