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.
出处
《华东理工大学学报（自然科学版）》
CAS
CSCD
北大核心
2004年第2期194-198,共5页
Journal of East China University of Science and Technology
基金
上海市自然科学基金(01ZD14014)
关键词
蚁群算法
近邻准则
故障诊断
故障识别
ant colony algorithm
near-neighborhood criteria
fault diagnosis
fault identification
