Automatica (Journal of IFAC)
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
The Self-organizing map as a tool in knowledge engineering
Pattern recognition in soft computing paradigm
Information Visualization and Visual Data Mining
IEEE Transactions on Visualization and Computer Graphics
Computers in Industry - Special issue: Soft computing in industrial applications
Internet-based remote supervision of industrial processes using self-organizing maps
Engineering Applications of Artificial Intelligence
Process Monitoring and Modeling Using the Self-Organizing Map
Integrated Computer-Aided Engineering
Expert Systems with Applications: An International Journal
From visual data exploration to visual data mining: a survey
IEEE Transactions on Visualization and Computer Graphics
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Bankruptcy trajectory analysis on french companies using self-organizing map
EPIA'11 Proceedings of the 15th Portugese conference on Progress in artificial intelligence
Development of a soldering quality classifier system using a hybrid data mining approach
Expert Systems with Applications: An International Journal
Monitoring industrial processes with SOM-based dissimilarity maps
Expert Systems with Applications: An International Journal
Clustering and visualization of bankruptcy trajectory using self-organizing map
Expert Systems with Applications: An International Journal
A Framework For State Transitions On The Self-Organizing Map: Some Temporal Financial Applications
International Journal of Intelligent Systems in Accounting and Finance Management
Exploiting the self-organizing financial stability map
Engineering Applications of Artificial Intelligence
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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.