Abstract
This paper presents a monitoring methodology to identify complex systems faults. This methodology combines the production of meaningful error signals (residuals) obtained by comparison between the model outputs and the system outputs, with a posterior fuzzy classification. In a first off-line phase (learning) the classification method characterises each fault. In the recognition phase, the classification method identifies the faults. The chose classification method permits to characterize faults non included in the learning data. This monitoring process avoids the problem of defining thresholds for faults isolation. The residuals analysis and not the system variables themselves, permit us to separate fault recognition from system operation point influence. The paper describes the proposed methodology using a benchmark of a two interconnected tanks system.
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Aguilar-Martin, J., Isaza, C., Diez-Lledo, E., LeLann, M.V., Vilanova, J.W. (2007). Process Monitoring Using Residuals and Fuzzy Classification with Learning Capabilities. In: Castillo, O., Melin, P., Ross, O.M., Sepúlveda Cruz, R., Pedrycz, W., Kacprzyk, J. (eds) Theoretical Advances and Applications of Fuzzy Logic and Soft Computing. Advances in Soft Computing, vol 42. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72434-6_28
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DOI: https://doi.org/10.1007/978-3-540-72434-6_28
Publisher Name: Springer, Berlin, Heidelberg
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