Data-driven Process Monitoring Method Based on Dynamic Component Analysis

摘要：

A novel data-driven process monitoring method based on dynamic independent component analysis-principle com-ponent analysis (DICA-DPCA)is proposed to compensate for shortcomings in the conventional component analysis based mon-itoring methods.The primary idea is to rst augment the measured data matrix to take the process dynamic into account.Then perform independent component analysis (ICA)and principle component analysis (PCA)on the augmented data to capture both the non-Gaussian and Gaussian process information.Finally,a combined monitoring statistic is proposed by support vector data description (SVDD)with its control limit being determined by bootstrap quantile estimation method to lessen monitoring work-load.The Tennessee Eastman process is used to demonstrate the improved monitoring performance of the proposed mechanism in comparison with existing component analysis based monitoring methods,including PCA,ICA,ICA-PCA,dynamic PCA,and dynamic ICA.

作者: ZHANG Guangming LI Ning LI Shaoyuan

作者单位: Department of Automation,Shanghai Jiao Tong University,and Key Laboratory of System Control and Information Processing,Ministry of Education of China,Shanghai 200240,P.R.China

会议类型: 国际会议

会议名称: The 30th Chinese Control Conference(第三十届中国控制会议)

会议地点: 烟台

会议语种:英文

页码: 1-6

在线出版日期: 2011-07-01（万方平台首次上网日期，不代表论文的发表时间）