重庆大学学报2018，Vol.41Issue(1)：88-98,11.DOI:10.11835/j.issn.1000-582X.2018.01.010
改进测度下的模糊C均值三元催化器故障诊断方法
Fault diagnosis of three-way catalytic converter using improved fuzzy C-means clustering
摘要
Abstract
The model precision of three-way catalytic converter is restricted by its complex physical and chemical reaction,which limits the accuracy of fault diagnosis based on its reaction model.To solve this problem,we propose a fault diagnosis method using improved fuzzy C-means (FCM) clustering.The method includes fault feature extraction and optimization using fractional Fourier transform (FRFT),dimensionality reduction of fractal feature using kernel entropy component analysis(KECA) and FCM fault feature clustering based on improved similarity measure.Firstly,we obtain the detailed features of different fault conditions from time domain to frequency domain using FRFT,then select the optimal FRFT order by particle swarm optimization (PSO) algorithm and these high-dimensional FRFT features with optimal order are transformed into fractal feature vectors through the fractal operator.Next,these fractal feature vectors dimensionality is reduced with KECA.At last,the reduced feature vectors are submitted to the improved FCM for fault clustering analysis.Numerical experiment results show that compared with the FCM method of Euclidean distance or cosine distance,the proposed method could obtain better fault identification result.关键词
三元催化器/故障诊断/尾气排放/模糊聚类Key words
three-way catalytic converter/fault diagnosis/exhaust emission/fuzzy clustering分类
信息技术与安全科学引用本文复制引用
李鹏华,刘晶晶,冯辉宗,米怡..改进测度下的模糊C均值三元催化器故障诊断方法[J].重庆大学学报,2018,41(1):88-98,11.基金项目
国家自然科学基金资助项目(61403053) （61403053）
重庆高校优秀成果转化项目(KJZH14207).Supported by National Natural Science Foundation of China(61403053) and Achievement Transfer Program of Institutions of Higher Education in Chongqing(KJZH14207). （KJZH14207）
