摘要
本文提出一种神经网络与模糊逻辑相结合的故障诊断系统,该系统包括2个方面:模糊推理模块和规则学习模块。模糊推理规则记忆在网络的记忆层中,记忆节点的激活水平则反映了输入矢量与已记忆规则的匹配程度;规则学习模块通过自组织聚类过程自动生成规则。作为该诊断系统的一个应用实例,模拟了旋转主轴的故障诊断试验。
A fault fuzzy diagnostic system (FFDS) based on neural network and fuzzy logic hybrid is proposed. FFDS consists of two modes: a fuzzy inference mode and a rules learning mode. The fuzzy inference rules are stored in the memory layer. The excitation levels of the memory neurons reflect the matching degree between the input vector and the prototype rules. In the rules learning mode, the rules can be produced automatically through the cluster process. As a application case of this diagnostic system, the fault diagnosis experiment of the rotating axis is simulated.
基金
国家攀登计划认知科学(神经网络)重大项目资助
关键词
故障诊断
神经网络
模糊推理
Fault diagnosis, Neural networks, Fuzzy inference, Expert knowledge
