Next Article in Journal
The Application of Dielectric Spectroscopy and Biocalorimetry for the Monitoring of Biomass in Immobilized Mammalian Cell Cultures
Next Article in Special Issue
Computer-Aided Framework for the Design of Freeze-Drying Cycles: Optimization of the Operating Conditions of the Primary Drying Stage
Previous Article in Journal
A Combined Feed-Forward/Feed-Back Control System for a QbD-Based Continuous Tablet Manufacturing Process
Previous Article in Special Issue
A Novel ARX-Based Approach for the Steady-State Identification Analysis of Industrial Depropanizer Column Datasets
Article Menu

Export Article

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

An Algorithm for Finding Process Identification Intervals from Normal Operating Data

Department of Electrical Engineering, Linköping University, SE-581 83 Linköping, Sweden
ABB AB, Corporate Resarch, SE-721 78 Västerås, Sweden
Hella Fahrzeugkomponenten GmbH, 28199 Bremen, Germany
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]   |  


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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

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.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Processes EISSN 2227-9717 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top