基于改进C均值的故障诊断  

Fault Diagnosis Based on the Improved C-Means

作　　者：周德财[1] 夏士雄[1] 王志晓[1] 

机构地区：[1]中国矿业大学计算机科学与技术学院,徐州江苏221116

出　　处：《微电子学与计算机》2012年第11期120-122,共3页Microelectronics & Computer

基　　金：教育部博士点基金项目(20100095110003);中国矿业大学青年科技基金(2011QNB23)

摘　　要：本文提出改进的C均值算法.改进算法引入数据场模型,通过故障点在数据场中的势值找出噪声点并踢出,利用故障点在数据场中呈现的自然聚集特性选择初始聚类中心指导FCM聚类.仿真实验的结果表明,改进算法弥补了FCM算法的缺陷,提高了聚类的效率和准确性,在故障诊断过程中有很好的表现.This paper introduce data field model to FCM,and proposed an improved FCM algorithm.On one hand,the improved algorithm uses potential values of fault points in the data field to identify noise points and kicks them out;on the other hand,the improved algorithm utilizes the natural aggregation properties presented by fault points in the data field to select initial clustering centers.The simulation experiment results showed that the improved algorithm has a good performance in fault diagnosis process.The improved algorithm could make up the defects of traditional FCM algorithm,and improve the efficiency and accuracy of clustering.

关 键 词：模糊C均值 数据场 聚类 噪声点 故障诊断 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]