SCI和EI收录∣中国化工学会会刊

Chin.J.Chem.Eng. ›› 2015, Vol. 23 ›› Issue (1): 146-153.DOI: 10.1016/j.cjche.2014.10.012

• PROCESS SYSTEMS ENGINEERING • Previous Articles     Next Articles

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

Zhiqiang Geng1, Ke Yang1, Yongming Han1, Xiangbai Gu1,2   

  1. 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:2015-01-24 Published:2015-01-28
  • 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).

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

Zhiqiang Geng1, Ke Yang1, Yongming Han1, Xiangbai Gu1,2   

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

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

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