Fault detection of large-scale process control system with higher-order statistical and interpretative structural model

doi:10.1016/j.cjche.2014.10.012

Chinese Journal of Chemical Engineering ›› 2015, Vol. 23 ›› Issue (1): 146-153.DOI: 10.1016/j.cjche.2014.10.012

• 过程系统工程与过程安全 • 上一篇下一篇

Fault detection of large-scale process control system with higher-order statistical and interpretative structural model

Zhiqiang Geng¹, Ke Yang¹, Yongming Han¹, Xiangbai Gu^1,2

1 College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China;
2 Sinopec Engineering (Group) Co., Ltd., Beijing 100101, China

收稿日期:2013-05-07 修回日期:2013-09-12 出版日期:2015-01-28 发布日期:2015-01-24
通讯作者: Xiangbai Gu
基金资助:
Supported by the National Natural Science Foundation of China(61374166), the Doctoral Fund of Ministry of Education of China(20120010110010) and the Natural Science Fund of Ningbo(2012A610001).

Fault detection of large-scale process control system with higher-order statistical and interpretative structural model

Zhiqiang Geng¹, Ke Yang¹, Yongming Han¹, Xiangbai Gu^1,2

1 College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China;
2 Sinopec Engineering (Group) Co., Ltd., Beijing 100101, China

Received:2013-05-07 Revised:2013-09-12 Online:2015-01-28 Published:2015-01-24
Contact: Xiangbai Gu
Supported by:
Supported by the National Natural Science Foundation of China(61374166), the Doctoral Fund of Ministry of Education of China(20120010110010) and the Natural Science Fund of Ningbo(2012A610001).

摘要/Abstract

摘要： Nonlinear characteristic fault detection and diagnosis method based on higher-order statistical (HOS) is an effective data-driven method, but the calculation costs much for a large-scale process control system. An HOS-ISM fault diagnosis framework combining interpretative structural model (ISM) and HOS is proposed: (1) the adjacency matrix is determined by partial correlation coefficient; (2) the modified adjacency matrix is defined by directed graph with prior knowledge of process piping and instrument diagram; (3) interpretative structural for large-scale process control system is built by this ISM method; and (4) non-Gaussianity index, nonlinearity index, and total nonlinearity index are calculated dynamically based on interpretative structural to effectively eliminate uncertainty of the nonlinear characteristic diagnostic method with reasonable sampling period and data window. The proposed HOS-ISM fault diagnosis framework is verified by the Tennessee Eastman process and presents improvement for highly non-linear characteristic for selected fault cases.

关键词: High order statistics, Nonlinear characteristics diagnosis, Interpretative structural model, TE process

Abstract: Nonlinear characteristic fault detection and diagnosis method based on higher-order statistical (HOS) is an effective data-driven method, but the calculation costs much for a large-scale process control system. An HOS-ISM fault diagnosis framework combining interpretative structural model (ISM) and HOS is proposed: (1) the adjacency matrix is determined by partial correlation coefficient; (2) the modified adjacency matrix is defined by directed graph with prior knowledge of process piping and instrument diagram; (3) interpretative structural for large-scale process control system is built by this ISM method; and (4) non-Gaussianity index, nonlinearity index, and total nonlinearity index are calculated dynamically based on interpretative structural to effectively eliminate uncertainty of the nonlinear characteristic diagnostic method with reasonable sampling period and data window. The proposed HOS-ISM fault diagnosis framework is verified by the Tennessee Eastman process and presents improvement for highly non-linear characteristic for selected fault cases.

Key words: High order statistics, Nonlinear characteristics diagnosis, Interpretative structural model, TE process

Zhiqiang Geng, Ke Yang, Yongming Han, Xiangbai Gu. Fault detection of large-scale process control system with higher-order statistical and interpretative structural model[J]. Chinese Journal of Chemical Engineering, 2015, 23(1): 146-153.

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Fault detection of large-scale process control system with higher-order statistical and interpretative structural model

Fault detection of large-scale process control system with higher-order statistical and interpretative structural model

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