Fault diagnosis of chemical processes based on partitioning PCA and variable reasoning strategy

doi:10.1016/j.cjche.2016.04.015

›› 2016, Vol. 24 ›› Issue (7): 869-880.DOI: 10.1016/j.cjche.2016.04.015

• Process Systems Engineering and Process Safety • 上一篇下一篇

Fault diagnosis of chemical processes based on partitioning PCA and variable reasoning strategy

Guozhu Wang^1,2, Jianchang Liu^1,2, Yuan Li³, Cheng Zhang¹

1 College of Information Science and Engineering, Northeastern University, Shenyang 110819, China;
2 State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China;
3 Information Engineering School, Shenyang University of Chemical Technology, Shenyang 110142, China

收稿日期:2015-07-29 修回日期:2015-12-16 出版日期:2016-07-28 发布日期:2016-08-17
通讯作者: Guozhu Wang
基金资助:
Supported by the National Natural Science Foundation of China (61374137, 61490701, 61174119) and the State Key Laboratory of Integrated Automation of Process Industry Technology and Research Center of National Metallurgical Automation Fundamental Research Funds (2013ZCX02-03).

Fault diagnosis of chemical processes based on partitioning PCA and variable reasoning strategy

Guozhu Wang^1,2, Jianchang Liu^1,2, Yuan Li³, Cheng Zhang¹

1 College of Information Science and Engineering, Northeastern University, Shenyang 110819, China;
2 State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China;
3 Information Engineering School, Shenyang University of Chemical Technology, Shenyang 110142, China

Received:2015-07-29 Revised:2015-12-16 Online:2016-07-28 Published:2016-08-17
Supported by:
Supported by the National Natural Science Foundation of China (61374137, 61490701, 61174119) and the State Key Laboratory of Integrated Automation of Process Industry Technology and Research Center of National Metallurgical Automation Fundamental Research Funds (2013ZCX02-03).

摘要/Abstract

摘要： Fault detection and identification are challenging tasks in chemical processes, the aimof which is to decide out of control samples and find fault sensors timely and effectively. This paper develops a partitioning principal component analysis (PPCA) method for process monitoring. A variable reasoning strategy is proposed and applied to recognize multiple fault variables. Compared with traditional process monitoring methods, the PPCA strategy not only reflects the local behavior of process variation in each model (each direction of principal components), but also improves the monitoring performance through the combination of local monitoring results. Then, a variable reasoning strategy is introduced to locate fault variables. Unlike the contribution plot, this method locates normal and fault variables effectively, and gives initiatory judgment for ambiguous variables. Finally, the effectiveness of the proposed process monitoring and fault variable identification schemes is verified through a numerical example and TE chemical process.

关键词: Fault detection, Fault identification, Process monitoring, Partitioning PCA, Variable reasoning strategy

Abstract: Fault detection and identification are challenging tasks in chemical processes, the aimof which is to decide out of control samples and find fault sensors timely and effectively. This paper develops a partitioning principal component analysis (PPCA) method for process monitoring. A variable reasoning strategy is proposed and applied to recognize multiple fault variables. Compared with traditional process monitoring methods, the PPCA strategy not only reflects the local behavior of process variation in each model (each direction of principal components), but also improves the monitoring performance through the combination of local monitoring results. Then, a variable reasoning strategy is introduced to locate fault variables. Unlike the contribution plot, this method locates normal and fault variables effectively, and gives initiatory judgment for ambiguous variables. Finally, the effectiveness of the proposed process monitoring and fault variable identification schemes is verified through a numerical example and TE chemical process.

Key words: Fault detection, Fault identification, Process monitoring, Partitioning PCA, Variable reasoning strategy

Guozhu Wang, Jianchang Liu, Yuan Li, Cheng Zhang. Fault diagnosis of chemical processes based on partitioning PCA and variable reasoning strategy[J]. , 2016, 24(7): 869-880.

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Fault diagnosis of chemical processes based on partitioning PCA and variable reasoning strategy

Fault diagnosis of chemical processes based on partitioning PCA and variable reasoning strategy

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