Monitoring based on MIC-PCA and SVDD for Industrial Process

摘要：

　　Complex industrial processes are often non-linear and non-Gaussian,while the traditional principal component analysis(PCA)method assumes that the data are Gaussian and linear.In this paper,a novel process monitoring method based on maximum information coefficient-PCA(MIC-PCA)and support vector data description(SVDD)is proposed.First,the covariance matrix is replaced by the MIC matrix which can measure the non-linear correlation between the variables.Then the SVDD models are built in the principal component subspace(PCS)and the residual subspace(RS)to improve the monitoring of non-linear and non-Gaussian processes.Finally,the feasibility and effectiveness of the proposed method are validated by high-pressure and low-density polyethylene(LDPE)industrial process.

关键词： principal component analysis support vector data description maximal information coefficient process monitoring industrial process

作者: Zhongwei Wang Shuai Li Hong Song Xiaofeng Zhou

作者单位: Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang,China;Key Laboratory of Networ Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang,China;Key Laboratory of Networ

会议类型: 国际会议

会议名称: 2017 IEEE 2nd Advanced Information Technology,Electronic and Automation Control Conference(IAEAC 2017)(2017 IEEE 第2届先进信息技术、电子与自动化控制国际会议)

会议地点: 重庆

会议语种:英文

页码: 1210-1214

在线出版日期: 2017-03-25（万方平台首次上网日期，不代表论文的发表时间）