[1]WEN Tianzhu,XU Aiqiang,DENG Lu.A new negative selection algorithm based on Extenics and its application in fault diagnosis[J].CAAI Transactions on Intelligent Systems,2015,10(3):488-493.[doi:10.3969/j.issn.1673-4785.201402020]
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A new negative selection algorithm based on Extenics and its application in fault diagnosis

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 3 Page number: 488-493 Column: 学术论文—机器学习 Public date: 2015-06-25
Title:
A new negative selection algorithm based on Extenics and its application in fault diagnosis
Author(s):
WEN Tianzhu1;  XU Aiqiang2;  DENG Lu1
1. Graduate Student’s Brigade, Naval Aeronautical and Astronautical University, Yantai 264001, China;
2. Department of Scientific Research, Naval Aeronautical and Astronautical University, Yantai 264001, China
Keywords:
extenics; negative selection algorithm; detector generation; detector optimization; fault diagnosis
CLC:
TP18
DOI:
10.3969/j.issn.1673-4785.201402020
Abstract:
In this paper, the extension negative selection algorithm is proposed by fusing Extenics and negative selection algorithm, aiming at the problem that traditional diagnosis algorithm can hardly solve fault detection by using normal state data. The basic elements are adopted to describe the models of problem domain, detectors and training samples, the dependent function is used to define the affinity calculation formula, and the extension detector generation and optimization algorithm are designed. In the phase of extension detector generation, the mature detectors are taken through the self-tolerance and in the phase of extension detector optimization, less mature detectors are taken through merging the detectors. The influence of threshold value of the degree of affinity on the coverage rate and detection rate of detectors are discussed in the parameter analysis. Finally, the proposed algorithm is used for fault detection of an integrated display and control platform. The obtained mature detectors not only have less numbers and are non-redundant, but also have high detection rate. The results showed that the algorithm can solve the fault detection problem in the condition of no fault state data and the detection results are consistent with the practive.
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