Abstract
We present an unsupervised cognitive fault diagnosis framework for nonlinear dynamic systems working in the space of approximating models. The diagnosis system detects and classifies faults by relying on a fault dictionary that is empty at the beginning of the system’s life and is automatically populated as faults occur. Outliers are treated as separate instances until enough confidence is built and either are integrated in existing classes or promoted to a new faults class. Simulation results show the effectiveness of the proposed approach.
This research has been funded by the European Commissions 7th Framework Program, under grant Agreement INSFO-ICT-270428 (iSense).
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Alippi, C., Roveri, M., Trovò, F. (2012). A “Learning from Models” Cognitive Fault Diagnosis System. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33266-1_38
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DOI: https://doi.org/10.1007/978-3-642-33266-1_38
Publisher Name: Springer, Berlin, Heidelberg
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