Systematic rationalization approach for multivariate correlated alarms based on interpretive structural modeling and Likert scale

doi:10.1016/j.cjche.2015.11.009

Chinese Journal of Chemical Engineering ›› 2015, Vol. 23 ›› Issue (12): 1987-1996.DOI: 10.1016/j.cjche.2015.11.009

• 第25届中国过程控制会议专栏 • 上一篇下一篇

Systematic rationalization approach for multivariate correlated alarms based on interpretive structural modeling and Likert scale

Huihui Gao^1,2, Yuan Xu^1,2, Xiangbai Gu^1,2,3, Xiaoyong Lin^1,2, Qunxiong Zhu^1,2

1 College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China;
2 Engineering Research Center of Intelligent PSE, Ministry of Education in China, Beijing 100029, China;
3 Sinopec Engineering (Group) Co., LTD, Beijing 100101, China

收稿日期:2015-05-19 修回日期:2015-07-17 出版日期:2015-12-28 发布日期:2016-01-19
通讯作者: Yuan Xu, Qunxiong Zhu
基金资助:
Supported by the National Natural Science Foundation of China (61473026, 61104131) and the Fundamental Research Funds for the Central Universities (JD1413).

Systematic rationalization approach for multivariate correlated alarms based on interpretive structural modeling and Likert scale

Huihui Gao^1,2, Yuan Xu^1,2, Xiangbai Gu^1,2,3, Xiaoyong Lin^1,2, Qunxiong Zhu^1,2

1 College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China;
2 Engineering Research Center of Intelligent PSE, Ministry of Education in China, Beijing 100029, China;
3 Sinopec Engineering (Group) Co., LTD, Beijing 100101, China

Received:2015-05-19 Revised:2015-07-17 Online:2015-12-28 Published:2016-01-19
Contact: Yuan Xu, Qunxiong Zhu
Supported by:
Supported by the National Natural Science Foundation of China (61473026, 61104131) and the Fundamental Research Funds for the Central Universities (JD1413).

摘要/Abstract

摘要： Alarmflood is one of themain problems in the alarmsystems of industrial process. Alarmroot-cause analysis and alarmprioritization are good for alarmflood reduction. This paper proposes a systematic rationalization method for multivariate correlated alarms to realize the root cause analysis and alarm prioritization. An information fusion based interpretive structural model is constructed according to the data-driven partial correlation coefficient calculation and process knowledge modification. This hierarchical multi-layer model is helpful in abnormality propagation path identification and root-cause analysis. Revised Likert scale method is adopted to determine the alarmpriority and reduce the blindness of alarmhandling. As a case study, the Tennessee Eastman process is utilized to showthe effectiveness and validity of proposed approach. Alarmsystem performance comparison shows that our rationalization methodology can reduce the alarmflood to some extent and improve the performance.

关键词: Alarm rationalization, Root-cause analysis, Alarm priority, Interpretive structural modeling, Likert scale, Tennessee Eastman process

Abstract: Alarmflood is one of themain problems in the alarmsystems of industrial process. Alarmroot-cause analysis and alarmprioritization are good for alarmflood reduction. This paper proposes a systematic rationalization method for multivariate correlated alarms to realize the root cause analysis and alarm prioritization. An information fusion based interpretive structural model is constructed according to the data-driven partial correlation coefficient calculation and process knowledge modification. This hierarchical multi-layer model is helpful in abnormality propagation path identification and root-cause analysis. Revised Likert scale method is adopted to determine the alarmpriority and reduce the blindness of alarmhandling. As a case study, the Tennessee Eastman process is utilized to showthe effectiveness and validity of proposed approach. Alarmsystem performance comparison shows that our rationalization methodology can reduce the alarmflood to some extent and improve the performance.

Key words: Alarm rationalization, Root-cause analysis, Alarm priority, Interpretive structural modeling, Likert scale, Tennessee Eastman process

Huihui Gao, Yuan Xu, Xiangbai Gu, Xiaoyong Lin, Qunxiong Zhu. Systematic rationalization approach for multivariate correlated alarms based on interpretive structural modeling and Likert scale[J]. Chinese Journal of Chemical Engineering, 2015, 23(12): 1987-1996.

参考文献

[1] M.L. Bransby, J. Jenkinson, The Management of Alarm System, CRR 166/1998, Health and Safety Executive, 1998.

[2] B.R. Hollifield, E. Habibi, The Alarm Management Handbook: A comprehensive Guide, PAS, Huston, Texas, 2006.

[3] D.H. Rothenberg, Alarm Management for Process Control, Momentum Press, New York, 2009.

[4] EEMUA, Alarm Systems: A Guide to Design, Management, and Procurement, 3 ed. EEMUA, London, UK, 2013.

[5] ISA, Technical Report ANSI/ISA -18.2-2009, 2009.

[6] F. Yang, D. Xiao, Progress in root cause and fault propagation analysis of large-scale industrial processes, J. Control Sci. Eng. 2012 (2012) 1-10.

[7] M. Iri, K. Aoki, E. O'Shima, H. Matsuyama, An algorithm for diagnosis of system failures in the chemical process, Comput. Chem. Eng. 3 (1) (1979) 489-493.

[8] M. Ram Maurya, R. Rengaswamy, V. Venkatasubramanian, Application of signed digraphs-based analysis for fault diagnosis of chemical process flowsheets, Eng. Appl. Artif. Intell. 17 (5) (2004) 501-518.

[9] M. Ram Maurya, R. Rengaswamy, V. Venkatasubramanian, A signed directed graph and qualitative trend analysis-based framework for incipient fault diagnosis, Chem. Eng. Res. Des. 85 (10) (2007) 1407-1422.

[10] F. Yang, S.L. Shah, D. Xiao, SDG (Signed Directed Graph) based process description and fault propagation analysis for a tailings pumping process, Proceedings of the 13th IFAC Symposium on Automation in Mining, Mineral and Metal Processing, Cape Town, South Africa, 2010.

[11] H. Jiang, R. Patwardhan, S.L. Shah, Root cause diagnosis of plant-wide oscillations using the concept of adjacency matrix, J. Process Control 19 (8) (2009) 1347-1354.

[12] F. Dahlstrand, Consequence analysis theory for alarm analysis, Knowl.-Based Syst. 15 (1) (2002) 27-36.

[13] M. Schleburg, L. Christiansen, N.F. Thornhill, A. Fay, A combined analysis of plant connectivity and alarm logs to reduce the number of alerts in an automation system, J. Process Control 23 (6) (2013) 839-851.

[14] F. Yang, S.L. Shah, D. Xiao, T. Chen, Improved correlation analysis and visualization of industrial alarm data, ISA Trans. 51 (4) (2012) 499-506.

[15] Z. Yang, J. Wang, T. Chen, Detection of correlated alarms based on similarity coefficients of binary data, IEEE Trans. Autom. Sci. Eng. 10 (4) (2013) 1014-1025.

[16] M. Bauer, J.W. Cox, M.H. Caveness, J.J. Downs, N.F. Thornhill, Finding the direction of disturbance propagation in a chemical process using transfer entropy, IEEE Trans. Control Syst. Technol. 15 (1) (2007) 12-21.

[17] P. Duan, F. Yang, T. Chen, S.L. Shah, Direct causality detection via the transfer entropy approach, IEEE Trans. Control Syst. Technol. 21 (6) (2013) 2052-2066.

[18] P. Duan, F. Yang, S.L. Shah, T. Chen, Transfer zero-entropy and its application for capturing cause and effect relationship between variables, IEEE Trans. Control Syst. Technol. (2014) http://dx.doi.org/10.1109/TCST.2014.2345095.

[19] S. Dey, J.A. Stori, A Bayesian network approach to root cause diagnosis of process variations, Int. J. Mach. Tools Manuf. 45 (1) (2005) 75-91.

[20] S. Pradhan, R. Singh, K. Kachru, S. Narasimhamurthy, A Bayesian network based approach for root-cause-analysis in manufacturing process, Computational Intelligence and Security, 2007 International Conference on, IEEE 2007, pp. 10-14.

[21] P. Duan, F. Yang, S.L. Shah, T. Chen, Capturing Connectivity and Causality in Complex Industrial Processes, Springer, 2014.

[22] G. Weidl, A.L. Madsen, S. Israelson, Applications of object-oriented Bayesian networks for condition monitoring, root cause analysis and decision support on operation of complex continuous processes, Comput. Chem. Eng. 29 (9) (2005) 1996-2009.

[23] P. Duan, T. Chen, S.L. Shah, F. Yang, Methods for root cause diagnosis of plant-wide oscillations, AIChE J. 60 (6) (2014) 2019-2034.

[24] Y.Wan, F. Yang, N. Lv, H. Xu, H. Ye,W. Li, A.K. Usadi, Statistical root cause analysis of novel faults based on digraph models, Chem. Eng. Res. Des. 91 (1) (2013) 87-99.

[25] F. Yang, S. Shah, D. Xiao, Signed directed graph based modeling and its validation from process knowledge and process data, Int. J. Appl. Math. Comput. 22 (1) (2012) 41-53.

[26] L. Abele, M. Anic, T. Gutmann, J. Folmer, M. Kleinsteuber, B. Vogel-Heuser, Combining knowledgemodeling andmachine learning for alarmroot cause analysis, Manuf. Model. Manage. Control 7 (1) (2013) 1843-1848.

[27] J. Thambirajah, L. Benabbas, M. Bauer, N.F. Thornhill, Cause-and-effect analysis in chemical processes utilizing XML, plant connectivity and quantitative process history, Comput. Chem. Eng. 33 (2) (2009) 503-512.

[28] Y. Chang, F. Khan, S. Ahmed, A risk-based approach to design warning system for processing facilities, Process. Saf. Environ. Prot. 89 (5) (2011) 310-316.

[29] S. Ahmed, H.A. Gabbar, Y. Chang, F.I. Khan, Risk based alarm design: A systems approach, 2011 International Symposium on Advanced Control of Industrial Processes (ADCONIP), IEEE 2011, pp. 42-47.

[30] Q.X. Zhu, Z.Q. Geng, A new fuzzy clustering-ranking algorithm and its application in process alarm management, Chin. J. Chem. Eng. 13 (4) (2005) 477-483.

[31] Z. Geng, Q. Zhu, X. Gu, A fuzzy clustering-ranking algorithm and its application for alarm operating optimization in chemical processing, Process. Saf. Prog. 24 (1) (2005) 66-75.

[32] O.M. Foong, S. Sulaiman, D.R.B.A. Rambli, ALAP: Alarm prioritization system for oil refinery, Proceedings of theWorld Congress on Engineering and Computer Science, San Francisco, 2009.

[33] V. Ravi, R. Shankar, M.K. Tiwari, Productivity improvement of a computer hardware supply chain, Int. J. Product. Perform. Manag. 54 (4) (2005) 239-255.

[34] K. Govindan, M. Palaniappan, Q. Zhu, D. Kannan, Analysis of third party reverse logistics provider using interpretive structural modeling, Int. J. Prod. Econ. 140 (1) (2012) 204-211.

[35] M.F. Ansari, R.K. Kharb, S. Luthra, S.L. Shimmi, S. Chatterji, Analysis of barriers to implement solar power installations in India using interpretive structural modeling technique, Renew. Sustain. Energy Rev. 27 (2013) 163-174.

[36] S. Luthra, V. Kumar, S. Kumar, A. Haleem, Barriers to implement green supply chain management in automobile industry using interpretive structural modeling technique: An Indian perspective, J. Ind. Eng. Manag. 4 (2) (2011) 231-257.

[37] R. Likert, A technique for the measurement of attitudes, Arch. Psychol. 140 (1932) 1-55.

[38] M.Á. Gil, G. González-Rodríguez, Fuzzy vs. Likert scale in statistics, Combining Experimentation and Theory, Springer, 2012 407-420.

[39] Y. Zuo, G. Yu, M.G. Tadesse, H.W. Ressom, Biological network inference using low order partial correlation, Methods 69 (3) (2014) 266-273.

[40] P. Charan, R. Shankar, R.K. Baisya, Analysis of interactions among the variables of supply chain performance measurement system implementation, Bus. Process. Manag. J. 14 (4) (2008) 512-529.

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

[42] D.R. Vinson, C. Georgakis, J. Fossy, Studies in plant-wide controllability using the Tennessee Eastman Challenge problem, the case for multivariable control, American Control Conference, Proceedings of the 1995, 1, IEEE 1995, pp. 250-254.

[43] P.R. Lyman, C. Georgakis, Plant-wide control of the Tennessee Eastman problem, Comput. Chem. Eng. 19 (3) (1995) 321-331.

[44] N. Lawrence Ricker, Decentralized control of the Tennessee Eastman challenge process, J. Process Control 6 (4) (1996) 205-221.

[45] N. Ye, T.J. McAvoy, K.A. Kosanovich, M.J. Piovoso, Plant-wide control using an inferential approach, American Control Conference, 1993, IEEE 1993, pp. 1900-1904.

[46] T.J. McAvoy, A methodology for screening level control structures in plantwide control systems[J], Comput. Chem. Eng. 22 (11) (1998) 1543-1552.

[47] T. Larsson, K. Hestetun, E. Hovland, S. Skogestad, Self-optimizing control of a largescale plant: The Tennessee Eastman process, Ind. Eng. Chem. Res. 40 (22) (2001) 4889-4901.

Systematic rationalization approach for multivariate correlated alarms based on interpretive structural modeling and Likert scale

Systematic rationalization approach for multivariate correlated alarms based on interpretive structural modeling and Likert scale

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