Improved performance of process monitoring based on selection of key principal components

doi:10.1016/j.cjche.2015.11.014

Chinese Journal of Chemical Engineering ›› 2015, Vol. 23 ›› Issue (12): 1951-1957.DOI: 10.1016/j.cjche.2015.11.014

• 第25届中国过程控制会议专栏 • 上一篇下一篇

Improved performance of process monitoring based on selection of key principal components

Bing Song, Yuxin Ma, Hongbo Shi

Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Ministry of Education, Shanghai 200237, China

收稿日期:2015-05-20 修回日期:2015-07-18 出版日期:2015-12-28 发布日期:2016-01-19
通讯作者: Hongbo Shi
基金资助:
Supported by the National Natural Science Foundation of China (No. 61374140) and Shanghai Pujiang Program (Project No. 12PJ1402200)

Improved performance of process monitoring based on selection of key principal components

Bing Song, Yuxin Ma, Hongbo Shi

Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Ministry of Education, Shanghai 200237, China

Received:2015-05-20 Revised:2015-07-18 Online:2015-12-28 Published:2016-01-19
Contact: Hongbo Shi
Supported by:
Supported by the National Natural Science Foundation of China (No. 61374140) and Shanghai Pujiang Program (Project No. 12PJ1402200)

摘要/Abstract

摘要： Conventional principal component analysis (PCA) can obtain low-dimensional representations of original data space, but the selection of principal components (PCs) based on variance is subjective, which may lead to information loss and poormonitoring performance. To address dimension reduction and information preservation simultaneously, this paper proposes a novel PC selection scheme named full variable expression.On the basis of the proposed relevance of variables with each principal component, key principal components can be determined. All the key principal components serve as a low-dimensional representation of the entire original variables, preserving the information of original data spacewithout information loss.A squaredMahalanobis distance,which is introduced as themonitoring statistic, is calculated directly in the key principal component space for fault detection. To test the modeling and monitoring performance of the proposed method, a numerical example and the Tennessee Eastman benchmark are used.

关键词: Principal component analysis, Information loss, Fault detection, Key principal component

Abstract: Conventional principal component analysis (PCA) can obtain low-dimensional representations of original data space, but the selection of principal components (PCs) based on variance is subjective, which may lead to information loss and poormonitoring performance. To address dimension reduction and information preservation simultaneously, this paper proposes a novel PC selection scheme named full variable expression.On the basis of the proposed relevance of variables with each principal component, key principal components can be determined. All the key principal components serve as a low-dimensional representation of the entire original variables, preserving the information of original data spacewithout information loss.A squaredMahalanobis distance,which is introduced as themonitoring statistic, is calculated directly in the key principal component space for fault detection. To test the modeling and monitoring performance of the proposed method, a numerical example and the Tennessee Eastman benchmark are used.

Key words: Principal component analysis, Information loss, Fault detection, Key principal component

Bing Song, Yuxin Ma, Hongbo Shi. Improved performance of process monitoring based on selection of key principal components[J]. Chinese Journal of Chemical Engineering, 2015, 23(12): 1951-1957.

Bing Song, Yuxin Ma, Hongbo Shi. Improved performance of process monitoring based on selection of key principal components[J]. Chin.J.Chem.Eng., 2015, 23(12): 1951-1957.

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Improved performance of process monitoring based on selection of key principal components

Improved performance of process monitoring based on selection of key principal components

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