Immune inspired Fault Detection and Diagnosis: A fuzzy-based approach of the negative selection algorithm and participatory clustering

被引：32

作者：

Silva, Guilherme Costa ^{[2
]}

Palhares, Reinaldo Martinez ^{[1
]}

Caminhas, Walmir Matos ^{[1
]}

机构：

[1] Univ Fed Minas Gerais, Dept Elect Engn, BR-31270901 Belo Horizonte, MG, Brazil

[2] Univ Fed Minas Gerais, Grad Program Elect Engn, BR-31270901 Belo Horizonte, MG, Brazil

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2012年 / 39卷 / 16期

关键词：

Fault Detection and Diagnosis; Artificial immune systems; Anomaly detection systems; QUANTITATIVE MODEL;

D O I：

10.1016/j.eswa.2012.04.066

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper describes an immune-inspired system based on an alternate theory about the self-nonself distinction theory, which defines the negative selection process as a mechanism of a fuzzy system based on the affinity between antigen and T-cells. This theory may provide a decision making tool which improves the generation of detectors or even define new data monitoring in order to detect an extreme variation of the system behavior, which means anomalies occurrences. Through these algorithms, tests are performed to detect faults of a DC motor. Upon detection of faults, a participatory clustering algorithm is used to classify these faults and tested to obtain the best set of parameters to achieve the most accurate clustering for these tests in the application being discussed in the article. (C) 2012 Elsevier Ltd. All rights reserved.

引用

页码：12474 / 12486

页数：13

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