首页 | 本学科首页   官方微博 | 高级检索  
     检索      

粒子群优化模糊聚类算法在煤气鼓风机组振动故障诊断的应用
引用本文:赵,欣.粒子群优化模糊聚类算法在煤气鼓风机组振动故障诊断的应用[J].重庆工商大学学报(自然科学版),2013,30(2):37-41.
作者姓名:  
作者单位:重庆工商大学计算机科学与信息工程学院,重庆,400067
摘    要:针对模糊C均值聚类算法容易陷入局部极值和对初始值敏感的缺点,提出了一种粒子群优化模糊聚类算法,该算法利用粒子群优化算法寻找最优聚类中心,运用WFCM进行加权模糊聚类,能较大提高聚类的有效性;将该算法应用于煤气鼓风机组振动故障诊断中进行诊断仿真,结果表明:该算法较大提高了故障诊断的正确率。

关 键 词:粒子群模糊聚类  煤气鼓风机组  故障诊断

Application of PSO Fuzzy Clustering Algorithm to Fault Diagnosis of Gas Blower Group Vibration
ZHAO Xin.Application of PSO Fuzzy Clustering Algorithm to Fault Diagnosis of Gas Blower Group Vibration[J].Journal of Chongqing Technology and Business University:Natural Science Edition,2013,30(2):37-41.
Authors:ZHAO Xin
Institution:ZHAO Xin(School of Computer Science and Information Engineering,Chongqing Technology and Business University, Chongqing 400067,China)
Abstract:According to the fault that fuzzy C-means clustering algorithm is easily involved in loal extreme and is sensitive to initial value,a kind of PSO fuzzy clustering algorighm is proposed,this algorithm searches the optimal clustering center based on PSO algorithm,uses WFCW to conduct weighted fuzzy cluster and is able to relatively more largely improve the validity of the cluster.This algorithm is used in diagnosis simulation in the fault diagosis of gas blower group vibation and the results show that this algorithm can relatively more largely improve the accurate rate of fault diagnosis.
Keywords:particle swarm optimization(PSO)  gas blower group  fault diagnosis
本文献已被 CNKI 万方数据 等数据库收录!
点击此处可从《重庆工商大学学报(自然科学版)》浏览原始摘要信息
点击此处可从《重庆工商大学学报(自然科学版)》下载免费的PDF全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号