Unsupervised pattern recognition methods for exploratory analysis of industrial process data

No Thumbnail Available
Journal Title
Journal ISSN
Volume Title
Doctoral thesis (article-based)
Checking the digitized thesis and permission for publishing
Instructions for the author
Degree programme
65, [66]
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.
self-organizing map, cluster analysis, exploratory data analysis, pattern recognition, process monitoring, industrial applications, unsupervised learning
Other note
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
Permanent link to this item