Abstract
Knowledge extraction from large amounts of data is an effective approach for analysis and monitoring of industrial processes. The self-organizing map (SOM) is a useful method for this purpose, because it is able to discover low-dimensional structures on high-dimensional spaces and produce a mapping on an ordered low-dimensional space that can be visualized and preserves the most important relationships. With the aim to extract knowledge about the dynamics of industrial processes, we define 2D SOM maps that represent dynamic features which are useful for usual tasks in control engineering such as the analysis of the time response, the coupling among variables, or the difficulties in control of MIMO (multiple-input and multiple-output) systems. Those new maps make it possible to discover, increase or confirm knowledge about the system, spanned through the entire operation range. A well-known quadruple-tank MIMO system was used to test the usefulness of these maps. First, we perform an analysis of the theoretical dynamic behaviors obtained from the physical equations of the system. After that, we carry out an analysis of experimental data from an industrial pilot plant.
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