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

doi:10.1016/j.cjche.2014.10.012

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 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:2015-01-24 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 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

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

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

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]. Chin.J.Chem.Eng., 2015, 23(1): 146-153.

References

[1] V. Venkatasubramanian, R. Rengaswamy, K. Yin, S.N. Kavuri, A review of process fault detection and diagnosis (Part I): Quantitative model-based methods, Comput. Chem. Eng. 27 (3) (2003) 293-311.

[2] V. Venkatasubramanian, R. Rengaswamy, K. Yin, S.N. Kavuri, A review of process fault detection and diagnosis (Part II): Qualitative models and search strategies, Comput. Chem. Eng. 27 (3) (2003) 313-326.

[3] V. Venkatasubramanian, R. Rengaswamy, K. Yin, S.N. Kavuri, A review of process fault detection and diagnosis (part III): Process history based methods, Comput. Chem. Eng. 27 (3) (2003) 327-346.

[4] Fu. Kechang, Wu. Tiejun, Feature subspace based KPCA and its application to fault detection and diagnosis, J. Chem. Ind. Eng. 57 (11) (2006) 2664-2669.

[5] Zhang Yingwei, Ma Chi, Fault diagnosis of nonlinear processes using multiscaleKPCA and multi-scale KPLS, Chem. Eng. Sci. 66 (1) (2011) 64-72.

[6] Chen Xinyi, Yan Xuefeng, Fault diagnosis in chemical process based on selforganizing map integrated with fisher discriminant analysis, Chin. J. Chem. Eng. 21 (4) (2013) 382-387.

[7] P.M. Frank, New developments using AI in fault diagnosis, Eng. Appl. Artif. Intell. 10 (1) (1997) 3-14.

[8] R.J. Patton, C.J. Lopez-Toribio, F.J. Uppal, Artificial intelligence approaches to fault diagnosis for dynamical systems, Int. J. Appl. Math. Comput. Sci. 9 (3) (1999) 471-518.

[9] M.A. Hussain, C.R. Che Hassan, K.S. Loh, K.W. Mah, Application of artificial intelligence technique in process fault diagnosis, J. Eng. Sci. Technol. 2 (3) (2007) 260-270.

[10] Jin Xin, Ren Xianbin, Zhou Liang, Overviewon intelligent technology of fault diagnosis, Foreign Electron. Meas. Technol. 28 (7) (2009) 30-32.

[11] Y.C. Kim, E.J. Powers, Digital bispectral analysis and its applications to nonlinear wave interactions, IEEE Trans. Plasma Sci. 7 (2) (1979) 120-131.

[12] C.L. Nikias, A. Petropulu, Higher-order Spectra: A Nonlinear Signal Processing Frame Work, Prentice-Hall, Englewood CliWs, N.J., 1993.

[13] J.W.A. Fackrell, Bispectral Analysis of Speech Signals, The University of Edinburgh, UK, 1996. (Ph.D.Thesis).

[14] M.A.A. Shoukat Choudhury, Sirish.L. Shah, Nina.F. Thornhillb, Diagnosis of poor control-loop performance using higher-order statistics, Automatica 40 (10) (2004) 1719-1728.

[15] M.A.A. Shoukat Choudhury, Troubleshooting plant wide oscillations using nonlinearity information, J. Chem. Eng. 24 (1) (2006) 50-60.

[16] Peng Danlin, Study on interpretative structural modeling and its application to summarized design, Eng. J. Wuhan Univ. 39 (1) (2006) 137-142.

[17] W.U. Wang Conglu, L.I. Shuqing Chao, Layer analysis of accident causes based oninterpretation structure model, MINING R & D 24 (5) (2004) 77-79.

[18] Tian Yanqing, Yang Zhenhong, Li Hua, S.H.A.N.G. Xuguang, Study on affecting factors of risks at workplace based on ISM and AH, China Saf. Sci. J. 21 (1) (2011) 140-146.

[19] M.D. Singh, R. Kant, Knowledge management barriers: an interpretive structural modeling approach, Industrial Engineering and Engineering Management, 2007 IEEE International Conference on Date of Conference, 2007, pp. 2091-2095.

[20] Yan Li kun, Application of correlation coefficient and biased correlation coefficient in related analysis, J. Yunnan Univ. Finance Econ. 19 (3) (2003) 78-80.

[21] Wang Haiyan, Yang Fangting, Liu Lu, Comparison and application of standardized regressive coefficient & partial correlation coefficient, Journal of Quantitative & Technical Economics (2006) 150-155.

[22] Jong-Min Kim, Yoon-Sung Jung, Taeryon Choi, Engin A. Sungura, Partial correlation with copula modeling, Computational Statistics and Data Analysis 55 (3) (2011) 1357-1366.

[23] Yang Fan, Member, SirishL. Shah, Deyun Xiao, Signed directed graph modeling of industrial processes and their validation by data-based methods, 2010 Conference on. IEEE, 2010, pp. 387-392.

[24] Jiang Hailei, Rohit Patwarhan, Sirish L. Shah, Root cause diagnosis of plantwideoscillations using the concept of adjacency matrix, J. Process Control 19 (8) (2009) 1347-1354.

[25] N.F. Thornhill, A. Horch, Advances and new directions in plant-wide disturbance detection and diagnosis, Control. Eng. Pract. 15 (10) (2007) 1196-1206.

[26] J.J. Downs, E.F. Vogel, A plant-wide industrial process control problem, Comput. Chem. Eng. 17 (3) (1993) 245-255.

[27] Xu. Peng Di, Zhu Qunxiong Yuan, Study and application of case-based extension fault diagnosis for chemical process, Chin. J. Chem. Eng. 21 (4) (2013) 366-375.

[28] Xie Xiang, Shi Hongbo, Multimode process monitoring based on fuzzy C-means in locality preserving projection subspace, Chin. J. Chem. Eng. 20 (6) (2012) 1174-1179.

[29] Song Kai, L.I. Wang Haiqing, F.E.N.G. Zhigang Ping, Quality based prioritized sensor fault monitoring methodology, Chin. J. Chem. Eng. 16 (4) (2008) 584-589.

[30] M. Golshan, R. Bozorgmehry boozarjomehry, M.R. Pishvaie, A new approach to real time optimization of the Tennessee Eastman challenge problem, Chem. Eng. J. 112 (2005) 33-44.

[31] Truls Larsson, Self-optimizing control of a large-scale plant: the Tennessee Eastman, process, Ind. Eng. Chem. Res. 40 (22) (2001) 4889-4901.

[32] Shen Yina, Steven X. Ding, A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process, J. Process Control 22 (9) (2012) 1567-1581.

[33] Zhang Ailing, ZhangWenying, Zhang Duanjin, Survey on fault detection and diagnosis for control systems, Systems Engineering and Electronics 29 (4) (2007) 659-664.

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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