Supervision of Industrial Processes using Self Organizing Maps

Supervision of Industrial Processes using Self Organizing Maps

Ignacio Díaz (Universidad de Oviedo, Spain), Abel A. Cuadrado (Universidad de Oviedo, Spain), Alberto B. Diez (Universidad de Oviedo, Spain), Manuel Domínguez (Instituto de Automática y Fabricación, Universidad de León, Spain), Juan J. Fuertes (Instituto de Automática y Fabricación, Universidad de León, Spain) and Miguel A. Prada (Instituto de Automática y Fabricación, Universidad de León, Spain)
DOI: 10.4018/978-1-4666-1806-0.ch011
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The objective of this chapter is to present, in a comprehensive and unified way, a corpus of data and knowledge visualization techniques based on the Self-Organizing Map (SOM). These techniques allow exploring the behavior of the process in a visual and intuitive way through the integration of existing process-related knowledge with information extracted from data, providing new ways for knowledge discovery. With a special focus on the application to process supervision and modeling, the chapter reviews well known techniques –such as component planes, u-matrix, and projection of the process state– but also presents recent developments for visualizing process-related knowledge, such as fuzzy maps, local correlation maps, and model maps. It also introduces the maps of dynamics, which allow users to visualize the dynamical behavior of the process on a local model basis, in a seamless integration with the former visualizations, making it possible to confront all them for discovery of new knowledge.
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In the last decade, there has been an increasing interest in visualization techniques for multidimensional data (Card et al., 1999; Vesanto, 1999; Keim, 2002; Ferreira de Oliveira et al.; 2003; Kreuseler et al., 2002). Information visualization approaches have been successfully used in scenarios similar to that described in the introduction, allowing to integrate vast amounts of data with available prior knowledge in a user centric fashion, by transforming data and information into a visual representation to exploit the pattern recognition capabilities of the human visual system.

One approach for multidimensional data visualization is the so-called dimensionality reduction (DR) approach. The DR approach consists of obtaining a dimension reduction mapping which projects, in a continuous way, points from the data space onto a 2D or 3D space, which can be visualized, without losing significant information. Once all information is presented in a visual way, the human ability for detection, reasoning and inference with complex patterns can be exploited for process understanding and discovery of new knowledge. These mappings serve as a bridge between the high dimensional space of process data and 2D visualizations –actually maps of the process–, that allow the human to operate with reasoning tools valid in the data space, such as rules or theoretical models –e.g. mechanical or electrical equations– in an insightful way, boosting his ability to find new relationships and, in sum, to discover new knowledge.

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