Abstract
This paper suggests an approach for fault detection and diagnosis capable to detect new operation modes online. The approach relies upon an evolving fuzzy classifier able to incorporate new operational information using an incremental unsupervised clustering procedure. The efficiency of the approach is verified in fault detection and diagnosis of an induction machine. Experimental results suggest that the approach is a promising alternative for fault diagnosis of dynamic systems when there is no a priori information about all failure modes. It is also attractive for incremental learning of diagnosis systems with streams of data.
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Lemos, A., Caminhas, W., Gomide, F. (2010). Fuzzy Multivariable Gaussian Evolving Approach for Fault Detection and Diagnosis. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds) Computational Intelligence for Knowledge-Based Systems Design. IPMU 2010. Lecture Notes in Computer Science(), vol 6178. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14049-5_37
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DOI: https://doi.org/10.1007/978-3-642-14049-5_37
Publisher Name: Springer, Berlin, Heidelberg
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