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Data Mining of Process Data in Multivariable Systems
KTH, School of Electrical Engineering (EES).
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Performing system identification experiments in order to model control plantsin industry processes can be costly and time consuming. Therefore, with increasinglymore computational power available and abundant access to loggedhistorical data from plants, data mining algorithms have become more appealing.This thesis focuses on evaluating a data mining algorithm for multivariate processwhere the mined data can potentially be used for system identification.The first part of the thesis explores the effect many of the necessary user chosenparameters have on the algorithmic performance. In order to do this, a GUIdesigned with assisting in parameter selection is developed. The second partof the thesis evaluates the proposed algorithm’s performance by modelling asimulated process based on intervals found by the algorithm.The results show that the algorithm is particularly sensitive to the choice ofcut-off frequencies in the bandpass filter, threshold of the reciprocal conditionnumber and the Laguerre filter order. It is also shown that with the GUI itis possible to select parameters such that the algorithm performs satisfactoryand mines data relevant for system identification. Finally, the results show thatit’s possible to use the mined data in order to model a simulated process usingsystem identification techniques with good accuracy.

Abstract [sv]

Modellering av reglersystem i industriprocesser med hjälp av system identifieringsexperiment, kan vara både kostsammt och tidskrävande. Ökad tillgångtill stora volymer av historisk lagrad data och processorkraft har därmed väcktstort intresse för data mining algoritmer.Denna avhandling fokuserar på utvärderingen av en data minig algoritm för mulitvariablaprocesser där de utvunna data segmenten can potenitellt användasför system identifiering. Första delen av avhandlingen utforskar vilken effektalgoritmens många parametrar har på dess prestanda. För att förenkla valenav parametrarna, utveklades ett användargränsnitt. Den andra delen av avhandlingenutvärderar algoritmens prestanda genom att modellera en simuleradprocess som är baserad på de utvunna data segment.Resultaten visar att algoritmen är särskilt känslig mot valen av brytfrekvensernai bandpassfiltret, tröskel värdet för det reciproka konditions talet och ordernpå Laguerre filtret. Dessutom visar resultaten att det är, genom det utveckladeanvändargränssnittet, möjligt att välja parameter värden som ger godtyckligautvunna data segment. Slutgiltigen kan det konstateras att man kan medhög nogrannhet modellera en simulerad process med hjälp av de utvunna datasegmenten från algoritmen.

Place, publisher, year, edition, pages
2016.
Series
TRITA-EE, ISSN 1653-5146 ; 2016:161
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-201087OAI: oai:DiVA.org:kth-201087DiVA, id: diva2:1072526
Available from: 2017-02-08 Created: 2017-02-08 Last updated: 2017-02-08Bibliographically approved

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akash patel(12094 kB)80 downloads
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