A new process monitoring method based on noisy time structure independent component analysis  

一种基于含噪时序结构独立元分析的过程监控方法(英文)

作　　者：蔡连芳 田学民 

机构地区：[1]College of Information and Control Engineering, China University of Petroleum

出　　处：《Chinese Journal of Chemical Engineering》2015年第1期162-172,共11页中国化学工程学报（英文版）

基　　金：Supported by the National Natural Science Foundation of China(61273160);the Natural Science Foundation of Shandong Province(ZR2011FM014);the Fundamental Research Funds for the Central Universities(12CX06071A);the Postgraduate Innovation Funds of China University of Petroleum(CX2013060)

摘　　要：Conventional process monitoring method based on fast independent component analysis(Fast ICA) cannot take the ubiquitous measurement noises into account and may exhibit degraded monitoring performance under the adverse effects of the measurement noises. In this paper, a new process monitoring approach based on noisy time structure ICA(Noisy TSICA) is proposed to solve such problem. A Noisy TSICA algorithm which can consider the measurement noises explicitly is firstly developed to estimate the mixing matrix and extract the independent components(ICs). Subsequently, a monitoring statistic is built to detect process faults on the basis of the recursive kurtosis estimations of the dominant ICs. Lastly, a contribution plot for the monitoring statistic is constructed to identify the fault variables based on the sensitivity analysis. Simulation studies on the continuous stirred tank reactor system demonstrate that the proposed Noisy TSICA-based monitoring method outperforms the conventional Fast ICA-based monitoring method.Conventional process monitoring method based on fast independent component analysis（Fast ICA） cannot take the ubiquitous measurement noises into account and may exhibit degraded monitoring performance under the adverse effects of the measurement noises. In this paper, a new process monitoring approach based on noisy time structure ICA（Noisy TSICA） is proposed to solve such problem. A Noisy TSICA algorithm which can consider the measurement noises explicitly is firstly developed to estimate the mixing matrix and extract the independent components（ICs）. Subsequently, a monitoring statistic is built to detect process faults on the basis of the recursive kurtosis estimations of the dominant ICs. Lastly, a contribution plot for the monitoring statistic is constructed to identify the fault variables based on the sensitivity analysis. Simulation studies on the continuous stirred tank reactor system demonstrate that the proposed Noisy TSICA-based monitoring method outperforms the conventional Fast ICA-based monitoring method.

关 键 词：Process monitoring Independent component analysis Measurement noises KURTOSIS Mixing matrix Contribution plot Sensitivity analysis 

分 类 号：TQ050[化学工程]