Volume 14, Issue 4 (Journal of Control, V.14, N.4 Winter 2021)                   JoC 2021, 14(4): 93-105 | Back to browse issues page

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Taheri-Kalani J, Latif-Shabgahi G, Alyari Shooredeli M. Performance Assessment for Multivariate Alarm Systems Based on Markov Model. JoC. 2021; 14 (4) :93-105
URL: http://joc.kntu.ac.ir/article-1-624-en.html
1- Department of Electrical Engineering, K. N. Toosi University
Abstract:   (2061 Views)
Alarm systems are essential in safe operation of industrial plants. Since many process variables are interacting with each other, so in this paper, an approximate method is introduced to design and analysis of a multivariate alarm system. In this method, the alarm system is designed base on joint indices. The Joint FAR and Joint MAR are defined for a m-variable alarm system thanks to multivariate Markov scheme. In proposed method, the alarm joint indices are defined by solving a Linear Programing (LP) optimization problem. By defining joint indices, tuning of the alarm parameters (like, threshold and etc.) can be done by these indices instead of correlation analysis. In this paper, penalty scenario and Genetic algorithm are used for alarm generation, and parameter optimization in Tennessee Eastman (TE) Process. The results of proposed method are compared with other methods.  
Full-Text [PDF 986 kb]   (91 Downloads)    
Type of Article: Research paper | Subject: Special
Received: 2018/10/17 | Accepted: 2019/10/21 | ePublished ahead of print: 2020/10/5 | Published: 2021/02/19

References
1. [1] Izadi, Iman, Sirish L. Shah, David S. Shook, and Tongwen Chen. "An introduction to alarm analysis and design", IFAC Proceedings, Vol. 42, no. 8, pp: 645-650, 2009. [DOI:10.3182/20090630-4-ES-2003.00107]
2. [2] ISA, (Instrumentation, Systems & Automation Society). "Management of alarm systems for the process industries", North Carolina: ISA 18.02, 2009.
3. [3] EEMUA, (Engineering Equipment and Materials Users' Association). "Alarm systems: a guide to design, management and procurement", 3rd ed. London: EEMUA Publication 191, 2013.
4. [4] Afzal, Muhammad Shahzad, Tongwen Chen, Ali Bandehkhoda, and Iman Izadi. "Analysis and design of time-deadbands for univariate alarm systems", Control Engineering Practice, Vol. 71, pp:96-107, 2018. [DOI:10.1016/j.conengprac.2017.10.016]
5. [5] Liu, Jun, et al. "The intelligent alarm management system", IEEE software. Vol.20, no.2, pp.66-71, 2003. [DOI:10.1109/MS.2003.1184170]
6. [6] Srinivasan, R., et al. "Intelligent alarm management in a petroleum refinery: plant safety and environment", Hydrocarbon processing. Vol.83, no.11, pp.47-53, 2004.
7. [7] Simeu-Abazi, Zineb, Arnaud Lefebvre, and Jean-Pierre Derain. "A methodology of alarm filtering using dynamic fault tree", Reliability Engineering & System Safety. Vol.96, no.2, pp.257-266, 2011. [DOI:10.1016/j.ress.2010.09.005]
8. [8] Cheng, Yue, Iman Izadi, and Tongwen Chen. "Optimal alarm signal processing: Filter design and performance analysis", Automation Science and Engineering, IEEE Transactions on. Vol.10, no.2 pp.446-451, 2013. [DOI:10.1109/TASE.2012.2233472]
9. [9] Xu, Jianwei, et al. "Performance assessment and design for univariate alarm systems based on FAR, MAR, and AAD", Automation Science and Engineering, IEEE Transactions on. Vol.9, no.2 pp.296-307, 2012. [DOI:10.1109/TASE.2011.2176490]
10. [10] Adnan, Naseeb Ahmed, Yue Cheng, Iman Izadi, and Tongwen Chen. "Study of generalized delay-timers in alarm configuration", Journal of Process Control, Vol. 23, no. 3, pp.382-395, 2013. [DOI:10.1016/j.jprocont.2012.12.013]
11. [11] Wang, Jiandong, and Tongwen Chen. "An online method to remove chattering and repeating alarms based on alarm durations and intervals", Computers & Chemical Engineering, Vol.67, no.4, pp.43-52, 2014. [DOI:10.1016/j.compchemeng.2014.03.018]
12. [12] Xu, Xiaobin, Shibao Li, Xiaojing Song, Chenglin Wen, and Dongling Xu. "The optimal design of industrial alarm systems based on evidence theory", Control Engineering Practice. Vol.46, no.1, pp.142-156, 2016. [DOI:10.1016/j.conengprac.2015.10.014]
13. [13] Zeng, Zhiyong, Wen Tan, and Rong Zhou. "Performance assessment for generalized delay-timers in alarm configuration", Journal of Process Control, Vol. 57, pp:80-101, 2017. [DOI:10.1016/j.jprocont.2017.06.013]
14. [14] Afzal, Muhammad Shahzad, and Tongwen Chen. "Analysis and design of multimode delay-timers", Chemical Engineering Research and Design, Vol. 120, pp:179-193, 2017. [DOI:10.1016/j.cherd.2017.01.029]
15. [15] Wang, Jiandong, Fan Yang, Tongwen Chen, and Sirish L. Shah. "An overview of Industrial Alarm Systems: Main causes for alarm overloading, research status, and open problems", IEEE Transactions on Automation Science and Engineering. Vol.13, no. 2, pp.1045-1061, 2016. [DOI:10.1109/TASE.2015.2464234]
16. [16] جعفر طاهری کلانی، کوروش اصلان صفت، غلامرضا لطیف شبگاهی، " ارائه یک روش سیستماتیک برای طراحی و تحلیل یک سیستم هشدار تک متغیره مبتنی بر سناریوی پنالتی"، مجله کنترل، جلد 10، شماره 4، زمستان 1395.
17. [17] Taheri-Kalani, J., G. Latif-Shabgahi, and M. Alyari Shooredeli. "On the use of penalty approach for design and analysis of univariate alarm systems", Journal of Process Control, Vol. 69, pp:103-113, 2018. [DOI:10.1016/j.jprocont.2018.07.018]
18. [18] Hao, Zang, and Li Hongguang. "Optimization of process alarm thresholds: A multidimensional kernel density estimation approach", Process Safety Progress. Vol. 33, No. 3, pp:292-298, 2014. [DOI:10.1002/prs.11658]
19. [19] Han, Liu, Huihui Gao, Yuan Xu, and Qunxiong Zhu. "Combining FAP, MAP and correlation analysis for multivariate alarm thresholds optimization in industrial process", Journal of Loss Prevention in the Process Industries. Vol. 40, no.3, pp.471-478, 2016. [DOI:10.1016/j.jlp.2016.01.022]
20. [20] Zhang, Kai, Steven X. Ding, Yuri AW Shardt, Zhiwen Chen, and Kaixiang Peng. "Assessment of T 2-and Q-statistics for detecting additive and multiplicative faults in multivariate statistical process monitoring", Journal of the Franklin Institute, Vol.354, no.2, pp.668-688, 2016. [DOI:10.1016/j.jfranklin.2016.10.033]
21. [21] Yu, Yan, Di Zhu, Jiandong Wang, and Yan Zhao. "Abnormal data detection for multivariate alarm systems based on correlation directions", Journal of Loss Prevention in the Process Industries, Vol. 45, pp: 43-55, 2017. [DOI:10.1016/j.jlp.2016.11.011]
22. [22] Chen, Kuang, and Jiandong Wang. "Design of multivariate alarm systems based on online calculation of variational directions", Chemical Engineering Research and Design, Vol. 122, pp:11-21, 2017. [DOI:10.1016/j.cherd.2017.04.011]
23. [23] Xiong, Wanqi, Jiandong Wang, and Kuang Chen. "Multivariate alarm systems for time-varying processes using Bayesian filters with applications to electrical pumps", IEEE Transactions on Industrial Informatics, Vol.14, no.2, pp.504-513, 2017. [DOI:10.1109/TII.2017.2749332]
24. [24] Ching, Wai‐Ki, Eric S. Fung, and Michael K. Ng. "A multivariate Markov chain model for categorical data sequences and its applications in demand predictions", IMA Journal of Management Mathematics, Vol. 13, no. 3, pp:187-199, 2002. [DOI:10.1093/imaman/13.3.187]
25. [25] Wright, A.H. "Genetic algorithms for real parameter optimization", Foundations of genetic algorithms, Vol. 1, pp: 205-218, 1991. [DOI:10.1016/B978-0-08-050684-5.50016-1]

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