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Neural Computing and Applications

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01.07.2014 | Original Article

Motor fault diagnosis using negative selection algorithm

verfasst von: X. Z. Gao, X. Wang, K. Zenger

Erschienen in: Neural Computing and Applications | Ausgabe 1/2014

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Abstract

In this paper, we propose a novel multi-level negative selection algorithm (NSA)-based motor fault diagnosis scheme. The hierarchical fault diagnosis approach takes advantage of the feature signals of the healthy motors so as to generate the NSA detectors and further uses the analysis of the activated detectors for fault diagnosis. It can not only efficiently detect incipient motor faults, but also correctly identify the corresponding fault types. The applicability of our motor fault diagnosis method is examined using two real-world problems in computer simulations.

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Titel: Motor fault diagnosis using negative selection algorithm
verfasst von: X. Z. Gao
X. Wang
K. Zenger
Publikationsdatum: 01.07.2014
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 1/2014
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-013-1447-2

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