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Unsupervised pattern recognition methods for exploratory analysis of industrial process data

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Alhoniemi, Esa
dc.date.accessioned 2012-02-10T09:47:58Z
dc.date.available 2012-02-10T09:47:58Z
dc.date.issued 2002-12-13
dc.identifier.isbn 951-22-6093-X
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/2257
dc.description.abstract The rapid growth of data storage capacities of process automation systems provides new possibilities to analyze behavior of industrial processes. As existence of large volumes of measurement data is a rather new issue in the process industry, long tradition of using data analysis techniques in that field does not yet exist. In this thesis, unsupervised pattern recognition methods are shown to represent one potential and computationally efficient approach in exploratory analysis of such data. This thesis consists of an introduction and six publications. The introduction contains a survey on process monitoring and data analysis methods, exposing the research which has been carried out in the fields so far. The introduction also points out the tasks in the process management framework where the methods considered in this thesis - self-organizing maps and cluster analysis - can be benefited. The main contribution of this thesis consists of two parts. The first one is the use of the existing and development of novel SOM-based methods for process monitoring and exploratory data analysis purposes. The second contribution is a concept where cluster analysis is used to extract and identify operational states of a process from measured data. In both cases the methods have been applied in exploratory analysis of real data from processes in the wood processing industry. en
dc.format.extent 65, [66]
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher Helsinki University of Technology en
dc.publisher Teknillinen korkeakoulu fi
dc.relation.haspart Esa Alhoniemi, Jaakko Hollmén, Olli Simula, and Juha Vesanto (1999). Process Monitoring and Modeling Using the Self-Organizing Map. In Integrated Computer-Aided Engineering, Vol. 6, No. 1, pp. 3-14. [article1.pdf] © 1999 IOS Press. By permission.
dc.relation.haspart Juha Vesanto, Johan Himberg, Esa Alhoniemi, and Juha Parhankangas (1999). Self-organizing Map in Matlab: the SOM Toolbox. In Proceedings of the Matlab DSP Conference 1999, Espoo, Finland, November 16-17, pp. 35-40. [article2.pdf] © 1999 Comsol Oy. By permission.
dc.relation.haspart Esa Alhoniemi (2000). Analysis of Pulping Data Using the Self-Organizing Map. In Tappi Journal, Vol. 83, No. 7, p. 66. The paper is available in its entirety at TAPPI's web site at http://www.tappi.org/. [article3.pdf] © 2000 TAPPI. By permission.
dc.relation.haspart Juha Vesanto and Esa Alhoniemi (2000). Clustering of the Self-Organizing Map. In IEEE Transactions on Neural Networks, Vol. 11, No. 3, pp. 586-600. [article4.pdf] © 2000 IEEE. By permission.
dc.relation.haspart Esa Alhoniemi and Olli Simula (2001). Interpretation and Comparison of Multidimensional Data Partitions. In Proceedings of the 9th European Symposium on Artificial Neural Networks, Bruges, Belgium, April 25-27, pp. 277-282. [article5.pdf] © 2001 ESANN. By permission.
dc.relation.haspart Maija Federley, Esa Alhoniemi, Mika Laitila, Mika Suojärvi, and Risto Ritala (2002). State Management for Process Monitoring, Diagnostics and Optimization. In Pulp & Paper Canada, Vol. 103, No. 2, pp. 40-43. [article6.pdf] © 2002 Pulp and Paper Technical Association of Canada (PAPTAC). By permission.
dc.subject.other Computer science en
dc.title Unsupervised pattern recognition methods for exploratory analysis of industrial process data en
dc.type G5 Artikkeliväitöskirja fi
dc.description.version reviewed en
dc.contributor.department Department of Computer Science and Engineering en
dc.contributor.department Tietotekniikan osasto fi
dc.subject.keyword self-organizing map en
dc.subject.keyword cluster analysis en
dc.subject.keyword exploratory data analysis en
dc.subject.keyword pattern recognition en
dc.subject.keyword process monitoring en
dc.subject.keyword industrial applications en
dc.subject.keyword unsupervised learning en
dc.identifier.urn urn:nbn:fi:tkk-002270
dc.type.dcmitype text en
dc.type.ontasot Väitöskirja (artikkeli) fi
dc.type.ontasot Doctoral dissertation (article-based) en
dc.contributor.lab Laboratory of Computer and Information Science en
dc.contributor.lab Informaatiotekniikan laboratorio fi


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