Batch Process Monitoring with Dynamic-Static Joint Indicator Based on GSFA-GNPE

Traditional process monitoring methods ignore the time-series correlation between variables, and do not distinguish the dynamic relationship and static relationship between variables, resulting in poor monitoring effect. To solve these problems, a dynamic-static joint indicator monitoring method of...

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Autor principal:	ZHAO Xiaoqiang, MOU Miao
Formato:	article
Lenguaje:	ZH
Publicado:	Editorial Office of Journal of Shanghai Jiao Tong University 2021
Materias:	batch process process monitoring slow feature analysis neighborhood preserving embedding globality-locality bayesian inference Engineering (General). Civil engineering (General) TA1-2040 Chemical engineering TP155-156 Naval architecture. Shipbuilding. Marine engineering VM1-989
Acceso en línea:	https://doaj.org/article/173bdc88199f40ecafad7c6c5c7aefc5
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id	oai:doaj.org-article:173bdc88199f40ecafad7c6c5c7aefc5
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spelling	oai:doaj.org-article:173bdc88199f40ecafad7c6c5c7aefc52021-12-03T02:59:23ZBatch Process Monitoring with Dynamic-Static Joint Indicator Based on GSFA-GNPE1006-246710.16183/j.cnki.jsjtu.2020.290https://doaj.org/article/173bdc88199f40ecafad7c6c5c7aefc52021-11-01T00:00:00Zhttp://xuebao.sjtu.edu.cn/article/2021/1006-2467/1006-2467-55-11-1417.shtmlhttps://doaj.org/toc/1006-2467Traditional process monitoring methods ignore the time-series correlation between variables, and do not distinguish the dynamic relationship and static relationship between variables, resulting in poor monitoring effect. To solve these problems, a dynamic-static joint indicator monitoring method of batch process based on global slow feature analysis(GSFA)-global neighborhood preserving embedding (GNPE) is proposed in this paper, which can effectively extract dynamic global features and static global features. First, the dynamic and static characteristics of the process variables are evaluated. Variables with weak autocorrelation and cross-correlation are regarded as static variables, and the remaining variables are regarded as dynamic ones. Next, the GSFA and GNPE models are constructed for dynamic and static subspaces, respectively. Finally, the statistical information from each subspace is combined by using Bayesian inference to obtain the joint indicator of the mixed model to realize process monitoring. Finally, the proposed algorithm is applied to a numerical example and the penicillin fermentation simulation process for simulation verification. The results show that the proposed GSFA-GNPE algorithm has better fault detection effects than other algorithms.ZHAO Xiaoqiang, MOU MiaoEditorial Office of Journal of Shanghai Jiao Tong Universityarticlebatch processprocess monitoringslow feature analysisneighborhood preserving embeddingglobality-localitybayesian inferenceEngineering (General). Civil engineering (General)TA1-2040Chemical engineeringTP155-156Naval architecture. Shipbuilding. Marine engineeringVM1-989ZHShanghai Jiaotong Daxue xuebao, Vol 55, Iss 11, Pp 1417-1428 (2021)
institution	DOAJ
collection	DOAJ
language	ZH
topic	batch process process monitoring slow feature analysis neighborhood preserving embedding globality-locality bayesian inference Engineering (General). Civil engineering (General) TA1-2040 Chemical engineering TP155-156 Naval architecture. Shipbuilding. Marine engineering VM1-989
spellingShingle	batch process process monitoring slow feature analysis neighborhood preserving embedding globality-locality bayesian inference Engineering (General). Civil engineering (General) TA1-2040 Chemical engineering TP155-156 Naval architecture. Shipbuilding. Marine engineering VM1-989 ZHAO Xiaoqiang, MOU Miao Batch Process Monitoring with Dynamic-Static Joint Indicator Based on GSFA-GNPE
description	Traditional process monitoring methods ignore the time-series correlation between variables, and do not distinguish the dynamic relationship and static relationship between variables, resulting in poor monitoring effect. To solve these problems, a dynamic-static joint indicator monitoring method of batch process based on global slow feature analysis(GSFA)-global neighborhood preserving embedding (GNPE) is proposed in this paper, which can effectively extract dynamic global features and static global features. First, the dynamic and static characteristics of the process variables are evaluated. Variables with weak autocorrelation and cross-correlation are regarded as static variables, and the remaining variables are regarded as dynamic ones. Next, the GSFA and GNPE models are constructed for dynamic and static subspaces, respectively. Finally, the statistical information from each subspace is combined by using Bayesian inference to obtain the joint indicator of the mixed model to realize process monitoring. Finally, the proposed algorithm is applied to a numerical example and the penicillin fermentation simulation process for simulation verification. The results show that the proposed GSFA-GNPE algorithm has better fault detection effects than other algorithms.
format	article
author	ZHAO Xiaoqiang, MOU Miao
author_facet	ZHAO Xiaoqiang, MOU Miao
author_sort	ZHAO Xiaoqiang, MOU Miao
title	Batch Process Monitoring with Dynamic-Static Joint Indicator Based on GSFA-GNPE
title_short	Batch Process Monitoring with Dynamic-Static Joint Indicator Based on GSFA-GNPE
title_full	Batch Process Monitoring with Dynamic-Static Joint Indicator Based on GSFA-GNPE
title_fullStr	Batch Process Monitoring with Dynamic-Static Joint Indicator Based on GSFA-GNPE
title_full_unstemmed	Batch Process Monitoring with Dynamic-Static Joint Indicator Based on GSFA-GNPE
title_sort	batch process monitoring with dynamic-static joint indicator based on gsfa-gnpe
publisher	Editorial Office of Journal of Shanghai Jiao Tong University
publishDate	2021
url	https://doaj.org/article/173bdc88199f40ecafad7c6c5c7aefc5
work_keys_str_mv	AT zhaoxiaoqiangmoumiao batchprocessmonitoringwithdynamicstaticjointindicatorbasedongsfagnpe
_version_	1718373915595636736

Batch Process Monitoring with Dynamic-Static Joint Indicator Based on GSFA-GNPE

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