摘要
提出了一种基于知识的模糊神经网络并用于故障诊断.首先基于粗糙集对样本数据进行初步规则获取,并计算规则的依赖度和条件覆盖度,然后根据规则数目进行模糊神经网络结构部分设计,规则的依赖度和条件覆盖度用于设定网络初始权重,而用遗产算法对神经网络输出参数进行优化.这样的模糊神经网络称为基于知识的模糊神经网络.使用该网络对旋转机械常见故障进行诊断,结果表明,和一般模糊神经网络相比,该网络具有训练时间短而诊断率高的特点.
A novel knowledge-based fuzzy neural network （KBFNN） for fault diagnosis is presented. Crude roles were extracted and the corresponding dependent factors and antecedent coverage factors were calculated firstly from the diagnostic sample based on rough sets theory. Then the number of rules was used to construct partially the structure of a fuzzy neural network and those factors were implemented as initial weights, with fuzzy output parameters being optmized by genetic algorithm. Such fuzzy neural network was called KBFNN. This KBFNN was utilized to identify typical faults of rotating machinery. Diagnostic results show that it has those merits of shorter training time and higher right diagnostic level compared to general fuzzy neural networks.
出处
《应用数学和力学》
EI
CSCD
北大核心
2006年第1期89-97,共9页
Applied Mathematics and Mechanics
基金
国家"十五"科技攻关计划重点资助项目(2001BA204B05_KHKZ0009)
关键词
旋转机械
故障诊断
粗糙集
模糊集
遗传算法
基于知识的模糊神经网络
rotating machinery
fault diagnosis
rough sets theory
fuzzy sets theory
generic algorithm
knowledge-based fuzzy neural network
