摘要
数控机床故障诊断的核心任务是根据故障征兆,经过推理计算,自动识别故障发生原因,文章把粒子群算法和粗糙集运用于机械设备的故障诊断中,提出了基于粒子群的粗糙集约简的故障诊断知识获取、规则优化和故障识别。首先介绍了粗糙集属性约简,并把粒子群算法应用于粗糙集属性约简算法中,最后从对相关数据集的约简实验结果角度证明了算法的正确性和优越性。
After reasoning computing, fault diagnosis can automatically identify malfunction based on the fault symptoms, this paper uses the particle swarm algorithm and rough set in fault diagnosis, and pro- pose fault diagnosis knowledge acquisition, rules optimization and fault identification based on rough set attribute reduction of particle swarm. Firstly, this paper introduces the rough set attribute reduction, sec- ondly, the particle swarm algorithm is applied to rough set attribute reduction algorithm, finally, the cor- rectness and superiority of this algorithm is proved from the reduction experimental results of related data sets.
出处
《组合机床与自动化加工技术》
北大核心
2012年第8期51-54,共4页
Modular Machine Tool & Automatic Manufacturing Technique
基金
北京自然科学基金项目(9122003)
北京市教委专项(00791154430107)
北京市哲学社会科学规划项目(11JGB077)
关键词
粒子群
粗糙集
故障诊断
particle swarm
rough set
fault diagnosis
作者简介
武装（1970-），男，辽宁人，首都经济贸易大学信息学院讲师，博士，研究方向为人工智能、图形图像等，（E—mail）wuz9080@163．com。
