title: A Novel Method of Failure Sample Selection for Electrical Systems Using Ant Colony Optimization.
abstract: 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.
authors.affiliation: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; Department of Communication Engineering, Chengdu Technological University, Chengdu 611731, China.
authors.forename: Jian
authors.identifier: 0000-0003-0883-8185
authors.initials: J
authors.lastname: Xiong
authors.affiliation: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
authors.forename: Shulin
authors.initials: S
authors.lastname: Tian
authors.affiliation: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
authors.forename: Chenglin
authors.initials: C
authors.lastname: Yang
authors.affiliation: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
authors.forename: Cheng
authors.initials: C
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D000818:Animals; D001000:Ants / Q000502:physiology; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D011211:Electric Power Supplies; D019544:Equipment Failure Analysis; D006801:Humans; D011210:Power Plants; D011336:Probability