文献类型：期刊文献

中文题名：基于蚁群算法的故障识别

英文题名：Research on Fault Identification Based on Ant Colony Algorithm

作者：孙京诰[1];李秋艳[2];杨欣斌[1];黄道[1]

机构：[1]华东理工大学工业自动化国家工程中心分部,上海200237;[2]上海氯碱化工股份有限公司,上海200241

年份：2004

卷号：30

期号：2

起止页码：194

中文期刊名：华东理工大学学报（自然科学版）

外文期刊名：Journal of East China University of Science and Technology

收录：CSTPCD;;EI(收录号：2004268239453);Scopus;北大核心:【北大核心2000】；CSCD:【CSCD2011_2012】；

基金：上海市自然科学基金(01ZD14014)

语种：中文

中文关键词：蚁群算法;近邻准则;故障诊断;故障识别

外文关键词：ant colony algorithm; near-neighborhood criteria; fault diagnosis; fault identification

摘要：提出了一种新的基于蚁群算法的故障诊断知识获取算法。该算法将故障诊断中故障的识别分类问题转化为求解带约束的最优化聚类问题,并应用改进的蚁群算法,基于群体的协作与学习求解这一聚类问题。将该方法应用于一化学反应器的故障诊断过程,结果表明该算法具有实现简单、收敛速度快、本质分布式并行性、鲁棒性强以及故障识别结果可靠等优点。
In this paper a new kind of automated fault diagnosis knowledge acquistion algorithm is proposed based on modified ant colony algorithm. The problem of fault identification and classification is translated to a constrained optimized clustering problem under certain conditions in this algorithm. And a modified ant colony algorithm, based on multi-agent cooperation and learning, is applied to solve this clustering problem. It is used to the process of fault identification and classification for fault diagnosis of a chemical reactor. The results show that the algorithm has the advantages of high parallel, high effective of computing, rapid convergence, robust and credibility of the identification result.