A new approach to exploratory analysis of system dynamics using SOM. Applications to industrial processes

The self-organizing map (SOM) constitutes a powerful method for exploratory analysis of process data that is based on the so-called dimension reduction approach. The SOM algorithm defines a smooth non-linear mapping from a high-dimensional input space onto a low-dimensional output space (typically 2D) that preserves the most significant information about the input data distribution. This mapping can be used to obtain 2D representations (component planes, u-matrix, etc.) of the process variables that reveal the main static relationships, allowing to exploit available data and process-related knowledge in an efficient way for supervision and optimization purposes. In this work we present a complementary methodology to represent also the process dynamics in the SOM visualization, using maps in which every point represents a local dynamical behavior of the process and that, in addition, are consistent with the component planes of the process variables. The proposed methodology allows in this way to find relationships between the process variables and the process dynamics, opening important ways for the exploratory analysis of the dynamic behavior in non-linear and non-stationary processes. Experimental results from real data of two different industrial processes are also described, showing the possibilities of the proposed approach.