Visual dynamic model based on self-organizing maps for supervision and fault detection in industrial processes

Visual data mining techniques have experienced a growing interest for processing and interpretation of the large amounts of multidimensional data available in current industrial processes. One of the approaches to visualize data is based on self-organizing maps (SOM), which define a projection of the input space onto a 2D or 3D space that can be used to obtain visual representations. Although these techniques have been usually applied to visualize static relations among the process variables, they have proven to be very useful to display dynamic features of the processes. In this work, an approach based on the SOM to model the dynamics of multivariable processes is presented. The proposed method identifies the process conditions (clusters) and the probabilities of transition among them, using the trajectory followed by the input data on the 2D visualization space. Furthermore, a new method of residual computation for fault detection and identification that uses the dynamic information provided by the model of transitions is proposed. The proposed method for modeling and fault identification has been applied to supervise a real industrial plant and the results are included.