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Neural Computing and Applications

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01.10.2009 | Original Article

Clonal optimization-based negative selection algorithm with applications in motor fault detection

verfasst von: X. Z. Gao, S. J. Ovaska, X. Wang, M.-Y. Chow

Erschienen in: Neural Computing and Applications | Ausgabe 7/2009

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Abstract

The Negative Selection Algorithm (NSA) and clonal selection method are two typical kinds of artificial immune systems. In this paper, we first introduce their underlying inspirations and working principles. It is well known that the regular NSA detectors are not guaranteed to always occupy the maximal coverage of the nonself space. Therefore, we next employ the clonal optimization method to optimize these detectors so that the best anomaly detection performance can be achieved. A new motor fault detection scheme using the proposed NSA is also presented and discussed. We demonstrate the efficiency of our approach with an interesting example of motor bearings fault detection, in which the detection rates of three bearings faults are significantly improved.

Vorheriger Artikel A block-diagonal recurrent fuzzy neural network for system identification

Nächster Artikel Leave-one-out bounds for support vector ordinal regression machine

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Titel: Clonal optimization-based negative selection algorithm with applications in motor fault detection
verfasst von: X. Z. Gao
S. J. Ovaska
X. Wang
M.-Y. Chow
Publikationsdatum: 01.10.2009
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 7/2009
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-009-0276-9

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