Abstract
Use of historical logged data can be considered for system identification if performing dedicated experiments is not possible. Continuously operated plants are examples of processes where experiments for system identification are typically restricted due to a possibly negative impact on production. However, process variables are logged for long periods of time which results in large databases that are a valuable source of information for model estimation. Automatic selection of informative data intervals can support system identification when use of logged process data is addressed. A new method is presented that differs in several aspects from current approaches. Firstly, interval bounding is performed using the gradient of a norm associated to the resulting information matrix which decreases interval misdetection. Secondly, process data do not need to be normalized for change detection. Thirdly, an instrumental variables identification method is used which offers robustness to autocorrelated noise. Lastly, the proposed selection technique can be applied to multivariate processes. The performance of the proposed method is demonstrated in a case study implemented in a lab-scale chemical plant.
Zusammenfassung
Aufgezeichnete Daten können für die Systemidentifikation verwendet werden, falls die Durchführung gezielter Experimenten zur Datengewinnung eingeschränkt ist. Letzteres ist oft in kontinuierlich betriebenen Produktionsanlagen der Fall, da mögliche negative Auswirkung auf die Produktion vermieden werden sollen. Prozessgrößen werden jedoch häufig über Jahre erfasst, was zu großen Datenbeständen führt. Diese stellen eine wertvolle Informationsquelle für die datengetriebene Modellierung dar. Eine manuelle Auswahl der meist seltenen informativen Datensequenzen bedeutet einen sehr großen Aufwand, was eine automatisierte Auswahl attraktiv macht. In diesem Beitrag wird ein neues Verfahren vorgestellt, welches sich in mehreren Punkten von bekannten Verfahren abhebt: erstens werden die Grenzen informativer Intervalle basierend auf dem Gradienten einer Norm auf der Informationsmatrix ermittelt, wodurch die Häufigkeit fehlerhafter Detektionen verringert wird. Zweitens müssen die Prozessdaten nicht normiert werden. Drittens wird ein gegenüber autokorreliertem Rauschen robustes Hilfsvariablenverfahren angewandt. Viertens kann das vorgeschlagene Verfahren auf Mehrgrößenprozesse angewendet werden. In einer Fallstudie in der Prozessinsel einer Modellfabrik wird das vorgestellte Verfahren für industrienahe Signaleigenschaften demonstriert.
About the authors
M. Eng. David Arengas was research associate at the Department of Measurement and Control at the University of Kassel. His research interests include data selection for system identification and change detection.
Univ.-Prof. Dr.-Ing. Andreas Kroll is head of the Department of Measurement and Control at the University of Kassel. His research interests include nonlinear system identification and control, computational intelligence, remote sensing and sensor data fusion.
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