文献类型：期刊文献

中文题名：Performance monitoring of non-gaussian chemical processes with modes-switching using globality-locality preserving projection

英文题名：Performance monitoring of non-gaussian chemical processes with modes-switching using globality-locality preserving projection

作者：Xin Peng[1];Yang Tang[1];Wenli Du[1];Feng Qian[1]

机构：[1]Key Laboratory of Advanced Control and Optimization for Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

年份：2017

卷号：11

期号：3

起止页码：429

中文期刊名：Frontiers of Chemical Science and Engineering

外文期刊名：化学科学与工程前沿（英文版）

收录：CSTPCD;;Scopus;CSCD:【CSCD2017_2018】；PubMed;

语种：英文

中文关键词：non-Gaussian processes;subspace projection;independent component analysis;locality preserving projection;finite mixture model

外文关键词：non-Gaussian processes, subspace projection, independent component analysis, locality preserving projection, finite mixture model

摘要：In this paper, we propose a novel performance monitoring and fault detection method, which is based on modified structure analysis and globality and locality preserving （MSAGL） projection, for non-Gaussian processes with multiple operation conditions. By using locality preserving projection to analyze the embedding geometrical manifold and extracting the non-Gaussian features by independent component analysis, MSAGL preserves both the global and local structures of the data simultaneously. Furthermore, the tradeoff parameter of MSAGL is tuned adaptively in order to find the projection direction optimal for revealing the hidden structural information. The validity and effectiveness of this approach are illustrated by applying the proposed technique to the Tennessee Eastman process simulation under multiple operation conditions. The results demonstrate the advantages of the proposed method over conventional eigendecomposition-based monitoring methotis.
In this paper, we propose a novel performance monitoring and fault detection method, which is based on modified structure analysis and globality and locality preserving （MSAGL） projection, for non-Gaussian processes with multiple operation conditions. By using locality preserving projection to analyze the embedding geometrical manifold and extracting the non-Gaussian features by independent component analysis, MSAGL preserves both the global and local structures of the data simultaneously. Furthermore, the tradeoff parameter of MSAGL is tuned adaptively in order to find the projection direction optimal for revealing the hidden structural information. The validity and effectiveness of this approach are illustrated by applying the proposed technique to the Tennessee Eastman process simulation under multiple operation conditions. The results demonstrate the advantages of the proposed method over conventional eigendecomposition-based monitoring methotis.