Neural networks-based negative selection algorithm with applications in fault diagnosis

被引：3
|
作者：
Gao, XZ [1 ]
Ovaska, SJ [1 ]
Wang, X [1 ]
Chow, MY [1 ]
机构：
[1] Aalto Univ, Inst Intelligent Power Elect, FIN-02150 Espoo, Finland
来源：
2004 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN & CYBERNETICS, VOLS 1-7 | 2004年
关键词：
artificial immune systems; negative selection algorithm; neural networks; anomaly detection; fault diagnosis;
D O I：
10.1109/ICSMC.2004.1400869
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper we first propose a novel neural networks-based Negative Selection Algorithm (NSA). The principle and structure of our NSA are presented, and its training algorithm is derived Taking advantage of neural networks training, it has the distinguished capability of adaptation, which is well suited for dealing with practical problems under time-varying circumstances. A new fault diagnosis scheme using this NSA is next introduced, Two illustrative simulations of anomaly detection in chaotic time series and inner raceway fault diagnosis of bearings demonstrate the efficiency of the proposed neural networks-based NSA.
引用
收藏
页码：3408 / 3414
页数：7
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