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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Acknowledgments
This research work was funded by the Academy of Finland under Grants 135225, 127299, and 137837. The authors would like to thank the anonymous reviewers for their insightful comments and constructive suggestions that have improved the paper.
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Gao, X.Z., Wang, X. & Zenger, K. Motor fault diagnosis using negative selection algorithm. Neural Comput & Applic 25, 55–65 (2014). https://doi.org/10.1007/s00521-013-1447-2
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DOI: https://doi.org/10.1007/s00521-013-1447-2