Internet-based remote supervision of industrial processes using self-organizing maps

Authors:
M. Domínguez;J. J. Fuertes;P. Reguera;I. Díaz;A. A. Cuadrado
Affiliations:
Instituto de Automática y Fabricación, Área: Automática y Control, Escuela de Ingenierías Industrial e Informática, Campus Universitario de Vegazana, Universidad de L ...;Instituto de Automática y Fabricación, Área: Automática y Control, Escuela de Ingenierías Industrial e Informática, Campus Universitario de Vegazana, Universidad de L ...;Instituto de Automática y Fabricación, Área: Automática y Control, Escuela de Ingenierías Industrial e Informática, Campus Universitario de Vegazana, Universidad de L ...;Área de Ingeniería de Sistemas y Automática de la Universidad de Oviedo, Campus de Viesques, Edificio Departamental 2, Gijón 33204, Asturias, Spain;Área de Ingeniería de Sistemas y Automática de la Universidad de Oviedo, Campus de Viesques, Edificio Departamental 2, Gijón 33204, Asturias, Spain
Venue:
Engineering Applications of Artificial Intelligence
Year:
2007

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Abstract

In upcoming years, the strategies for maintenance, traceability, management and operation of productive processes will demand the use of novel information and communication technologies. Supervisory systems in these new scenarios will have to be able to integrate large volumes of information and knowledge coming both from local and remote points of large processes. These systems will therefore require new tools for management and integration of information and knowledge. In this work, the authors present an internet-based remote supervision system of industrial processes that incorporates powerful data and knowledge visualization tools based on self-organizing maps (SOM). This architecture adds an intermediate layer (database) to the well-known client and server layers, that isolates the client part from the industrial process, allowing to incorporate the required data management and neural network processing tasks. Remote users have access to advanced information visualization tools based on SOM, including both static visualizations, such as component planes or distance maps, and dynamical ones, such as residuals and state trajectory, allowing the interpretation of knowledge extracted by the SOM as well as the analysis and detection of possible abnormal conditions. This architecture has been validated through the supervision of an industrial pilot plant.