A Novel Method of Failure Sample Selection for Electrical Systems Using Ant Colony Optimization

Joint Authors

Xiong, Jian
Yang, Chenglin
Liu, Cheng
Tian, Shulin

Source

Computational Intelligence and Neuroscience

Issue

Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-7, 7 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2016-09-22

Country of Publication

Egypt

No. of Pages

7

Main Subjects

Biology

Abstract EN

The influence of failure propagation is ignored in failure sample selection based on traditional testability demonstration experiment method.

Traditional failure sample selection generally causes the omission of some failures during the selection and this phenomenon could lead to some fearful risks of usage because these failures will lead to serious propagation failures.

This paper proposes a new failure sample selection method to solve the problem.

First, the method uses a directed graph and ant colony optimization (ACO) to obtain a subsequent failure propagation set (SFPS) based on failure propagation model and then we propose a new failure sample selection method on the basis of the number of SFPS.

Compared with traditional sampling plan, this method is able to improve the coverage of testing failure samples, increase the capacity of diagnosis, and decrease the risk of using.

American Psychological Association (APA)

Xiong, Jian& Tian, Shulin& Yang, Chenglin& Liu, Cheng. 2016. A Novel Method of Failure Sample Selection for Electrical Systems Using Ant Colony Optimization. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1099677

Modern Language Association (MLA)

Xiong, Jian…[et al.]. A Novel Method of Failure Sample Selection for Electrical Systems Using Ant Colony Optimization. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-7.
https://search.emarefa.net/detail/BIM-1099677

American Medical Association (AMA)

Xiong, Jian& Tian, Shulin& Yang, Chenglin& Liu, Cheng. A Novel Method of Failure Sample Selection for Electrical Systems Using Ant Colony Optimization. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1099677

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1099677