Abstract
This paper presents the utilization of influence diagrams in the diagnosis of industrial processes. The diagnosis in this context signifies the early detection of abnormal behavior, and the selection of the best recommendation for the operator in order to correct the problem or minimize the effects. A software architecture is presented, based on the Elvira package, including the connection with industrial control systems. A simple experiment is presented together with the acquisition and representation of the knowledge.
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© 2004 Springer-Verlag Berlin Heidelberg
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Sánchez, B.M., Ibargüengoytia, P.H. (2004). Online Diagnosis Using Influence Diagrams. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds) MICAI 2004: Advances in Artificial Intelligence. MICAI 2004. Lecture Notes in Computer Science(), vol 2972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24694-7_56
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DOI: https://doi.org/10.1007/978-3-540-24694-7_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21459-5
Online ISBN: 978-3-540-24694-7
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