摘要
提出了一种基于蚁群优化算法的精密伺服转台故障诊断方法.根据现场观测建立了转台系统故障特征模式库.利用蚁群优化算法求解故障特征模式的最优分类问题,并定义敏感度和明确度来评价蚁群搜索到的诊断规则的分类性能,以减少故障特征信息中的冗余信息,使诊断规则得到约简.对某精密伺服转台的若干类故障诊断结果表明,该方法具有收敛速度快、鲁棒性强、诊断精度高和结果可靠等优点.
A new fault diagnosis method for high precision servo simulator is presented in this paper. Based on the field observation, a fault feature pattern base is built up. The ant colony optimization （ACO） is used for the optimal classification problem of the fault feature patterns, in which an index function based on the sensitivity and the specificity is defined to evaluate the performance of the diagnostic rule obtained by ACO, in order to decrease the redundant information of the fault features so that the diagnosis rules can be reduced. Diagnosis results of several types of faults of a real high precision servo simulator show that the diagnosis method based on ACO is characterized by fast convergence speed, strong robustness, high diagnosis accuracy, and reliable results.
出处
《自动化学报》
EI
CSCD
北大核心
2009年第6期780-784,共5页
Acta Automatica Sinica
基金
国家自然科学基金(60874037)
教育部博士点科研基金(200702870-50)
江苏省普通高校研究生科研创新计划(CX08B_091Z)
南京航空航天大学博士学位论文创新与创优基金(BCXJ08-06)资助~~
关键词
精密伺服转台
蚁群优化
故障诊断
数据挖掘
分类算法
High precision servo simulator, ant colony optimization （ACO）, fault diagnosis, data mining, classification algorithm
