信息与控制2017，Vol.46Issue(1)：41-45,5.DOI:10.13976/j.cnki.xk.2017.0041
基于改进模糊聚类的控制系统故障检测
Control System Fault Detection Based on Improved Fuzzy Clustering
摘要
Abstract
Based on control system characteristics for high-dimension,coupled,and redundant data,we propose a new method that combines dynamic principal component analysis with a weighted fuzzy C-mean clustering algorithm.By considering the system's dynamic characteristics,the data dimensions are reduced.We use the principal components as weights and discuss the degree that different characteristics contribute to the system.We use the fuzzy C-means clustering algorithm to obtain the clustering center of normal data and establish the weight difference model using the fault data for detection in the control system.The experimental results show that the accuracy and effectiveness of control system fault detection can be improved by this method.关键词
控制系统/故障检测/动态主元分析/模糊C均值聚类/加权差值模型Key words
control system/fault detection/dynamic principal component analysis/fuzzy C-means clustering/weight difference model分类
信息技术与安全科学引用本文复制引用
王印松,商丹丹,宋凯兵,李士哲..基于改进模糊聚类的控制系统故障检测[J].信息与控制,2017,46(1):41-45,5.基金项目
河北省自然科学基金资助项目(F2012502032) （F2012502032）
中央高校基本科研业务费专项资金面上项目(2014MS152) （2014MS152）
