Abstract
Three Knowledge Based Systems (KBS's), performing diagnosis and integrated in a Knowledge Based Supervisory System (KBSS), are presented. The systems work on line in a continuos process factory and one of them is routinely used at the control room. The paper summarises the conceptual framework that guided the design of the KBSS, describing later the fault identification module of each diagnostician. Specially relevant were the different approaches tried to deal with the dynamic nature of the problem, looking for a good trade off between expressiveness and simplicity of the final system. Some experimental results, obtained from actual system performance at a beet sugar factory, and major conclusions, are included.
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Alonso González, C., Pulido Junquera, B., Acosta, G. (1998). On line industrial diagnosis: An attempt to apply artificial intelligence techniques to process control. In: Mira, J., del Pobil, A.P., Ali, M. (eds) Methodology and Tools in Knowledge-Based Systems. IEA/AIE 1998. Lecture Notes in Computer Science, vol 1415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64582-9_813
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DOI: https://doi.org/10.1007/3-540-64582-9_813
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