Motor fault diagnosis using negative selection algorithm

被引:12
|
作者
Gao, X. Z. [1 ]
Wang, X. [1 ]
Zenger, K. [1 ]
机构
[1] Aalto Univ, Sch Elect Engn, Dept Automat & Syst Technol, Espoo 00076, Finland
来源
NEURAL COMPUTING & APPLICATIONS | 2014年 / 25卷 / 01期
基金
芬兰科学院;
关键词
Artificial immune systems (AIS); Negative selection algorithm (NSA); Fault detection; Fault diagnosis; Motors; Electrical machines; DETECTORS;
D O I
10.1007/s00521-013-1447-2
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:55 / 65
页数:11
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