Volume 15, Issue 1 (Journal of Control, V.15, N.1 Spring 2021)                   JoC 2021, 15(1): 127-138 | Back to browse issues page

XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Valipoori A, Latif shabgahi G. Representing an alarm system for processes with variables with mixture probability distribution. JoC. 2021; 15 (1) :127-138
URL: http://joc.kntu.ac.ir/article-1-752-en.html
1- Shahid Beheshti university
Abstract:   (751 Views)
Alarm systems play an important role in ensuring safety, and preventing event occurrence in industrial plants. One of the most important steps in alarm system designing is estimation of the proper probability density function (pdf). Conventional methods in alarm system designing like, dead-band and delay timers cannot be more effective in case of alarm variable with mixture pdf. This paper presents a new method to design an univariate alarm system with mixture pdf in alarm variables. In this paper three alarm performance indecis are derived for variables with gussian pdf. In proposed method, rasing and clearing alarms are based on the probability values corresponding to the instantaneous alarm variable values in the normal and abnormal pdfs (normal and abnormal reference models). The effectiveness of the proposed method is shown during some simulation and industrial case studies and its performance compared with Reset scenario in delay timers. In one of the case studies, the performance of the proposed method in the DAMADICS benchmark actuators has been investigated.
Full-Text [PDF 1001 kb]   (19 Downloads)    
Type of Article: Research paper | Subject: Special
Received: 2020/03/29 | Accepted: 2021/01/1 | ePublished ahead of print: 2021/01/23 | Published: 2021/05/22

References
1. [1] ISA, (Instrumentation, Systems & Automation Society). "Management of alarm systems for the process industries", North Carolina: ISA 18.02, 2009.
2. [2] EEMUA, (Engineering Equipment and Materials Users' Association). "Alarm systems: a guide to design, management and procurement", 3rd ed. London: EEMUA Publication 191, 2013.
3. [3] Su, J., et al., "A multi-setpoint delay-timer alarming strategy for industrial alarm monitoring", Journal of Loss Prevention in the Process Industries, vol.54, pp. 1-9, 2018. [DOI:10.1016/j.jlp.2018.02.004]
4. [4] Taheri-Kalani, J., Latif-Shabgahi, G. and Alyari-Shooredeli, M., "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]
5. [5] Lin, J., et al., "A generalized alarm delay-timer's performance indices computing method", Systems Science & Control Engineering, vol.6, pp. 297-304, 2018. [DOI:10.1080/21642583.2018.1554838]
6. [6] Aslansefat, K., et al., "Performance evaluation and design for variable threshold alarm systems through semi-Markov process", ISA Transactions, vol. 97, pp. 282-295, 2019. [DOI:10.1016/j.isatra.2019.08.015]
7. [7] وحید محمدزاده ایوقی، مهدی علیاری شوره‌دلی، "آنالیز حساسیت و طراحی سیستم هشدار تک‌متغیره بر مبنای تایمر تأخیر با لحاظ‌کردن خطای اندازه‌گیری "، مجله مهندسی مکانیک مدرس، جلد 19، شماره 5، صفحات 1155-1165، 1398.
8. [8] Kaced, R., Kouadri, A. and Baiche, K., "Designing alarm system using modified generalized delay-timer", Journal of Loss Prevention in the Process Industries, vol.61, pp. 40-48, 2019. [DOI:10.1016/j.jlp.2019.04.010]
9. [9] جعفر طاهری کلانی، غلامرضا لطیف شبگاهی، مهدی علیاری شوره‌دلی، وحید محمدزاده ایوقی، مهدی علیاری شوره‌دلی، " طراحی یک سیستم هشدار تک متغیره با رویکرد تایمرهای تأخیری مبتنی بر سناریوی آستانه چندگانه"، مجله مهندسی برق تبریز، جلد 49، شماره 3، صفحات 1153-1165، 1398.
10. [10] Wang, Z., et al., "Indexing and designing deadbands for industrial alarm signals", IEEE Transactions on Industrial Electronics, vol.66, no.10, pp. 8093-8103, 2019. [DOI:10.1109/TIE.2018.2885718]
11. [11] Afzal, M.S., Chen, A. and Izadi, I., "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]
12. [12] Tulsyan, A and Gopaluni, R.B., "Univariate model-based deadband alarm design for nonlinear processes ", Industrial & Engineering Chemistry Research, vol.58, No. 26, pp. 11295-11302, 2019. [DOI:10.1021/acs.iecr.9b00014]
13. [13] Montgomery, D.C., " Introduction to Statistical Quality", (5th Edition), Aug 2004, [Online] Available: https://www. wiley.com.
14. [14] Izadi, I., Shah, S.L., Shook, D.S., Kondaveeti, S.R. and Chen, T., "A framework for optimal design of alarm systems ", Journal of Process Control, vol.42, No. 8, pp. 651-656, 2011. [DOI:10.3182/20090630-4-ES-2003.00108]
15. [15] Chen, J. and Ron, J.P., "Robust model-based fault diagnosis for dynamic systems", (first Edition), [Online] Available: https://www.springer.com, 1998. [DOI:10.1007/978-1-4615-5149-2_9]
16. [16] Cheng, Y., Izadi, I. and Chen, T., " Optimal alarm signal processing: filter design and performance analysis ", IEEE Transactions on Automation Science and Engineering, vol.10, No. 2, pp. 446-451, 2013. [DOI:10.1109/TASE.2012.2233472]
17. [17] Yu. j. and Joe Qin. S., "Multimode process monitoring with bayesian inference-based finite gaussian mixture models", American Institute of Chemical Engineers, vol.54, no.7, pp. 1811-1829, 2008. [DOI:10.1002/aic.11515]
18. [18] Veracini, T., et al, "Fully unsupervised learning of gaussian mixtures for anomaly detection in hyperspectral imagery"ninth International Conference on Intelligent Systems Design and Applications, Pisa, Italy, Nov 2009. [DOI:10.1109/ISDA.2009.220]
19. [19] Yu, J., "Fault detection using principal components-based gaussian mixture model for semiconductor manufacturing processes", IEEE Transactions on Semiconductor Manufacturing, vol.24, no.3, pp. 432-444, 2011. [DOI:10.1109/TSM.2011.2154850]
20. [20] Choi, S.W., et al., "Fault detection based on a maximum-likelihood Principal Component Analysis (PCA) mixture", Industrial & Engineering Chemistry Research, vol.44, no.7, pp. 2316-2327, 2005. [DOI:10.1021/ie049081o]
21. [21] Marwala, T., Mahola, U. and Nelwamondo, F.V., "Hidden markov models and gaussian mixture models for bearing fault detection using fractals", The 2006 IEEE International Joint Conference on Neural Network Proceedings, Vancouver, BC, Canada. [DOI:10.1109/IJCNN.2006.247310]
22. [22] Wang, G.F., Li, Y.B. and Luo, Z.G., "Fault classification of rolling bearing based on reconstructed phase space and gaussian mixture model", Journal of Sound and Vibration, vol.323, no.3, pp. 1077-1089, 2009. [DOI:10.1016/j.jsv.2009.01.003]
23. [23] Bashi, A., Jilkov, V.P. and Li, X.R, "Fault detection for systems with multiple unknown modes and similar units and its application to HVAC", IEEE Transactions on Control Systems Technology, vol.19, no.5, pp. 957-968., 2011. [DOI:10.1109/TCST.2010.2062183]
24. [24] Yu, J., "A nonlinear kernel gaussian mixture model based inferential monitoring approach for fault detection and diagnosis of chemical processes", Chemical Engineering Science, vol.68, no.1, pp. 506-519, 2012. [DOI:10.1016/j.ces.2011.10.011]
25. [25] Yu, J., "A particle filter driven dynamic gaussian mixture model approach for complex process monitoring and fault diagnosis", Journal of Process Control, vol.22, no.4, pp. 778-788, 2012. [DOI:10.1016/j.jprocont.2012.02.012]
26. [26] Izadi, I., et al., "An introduction to alarm analysis and design. IFAC Proceedings Volumes, vol.42, no.8, pp. 645-650, 2009. [DOI:10.3182/20090630-4-ES-2003.00107]
27. [27] Wang, Z. and Scott, D.W., "Nonparametric Density Estimation for High-Dimensional Data - Algorithms and Applications, vol.11, 2019. [DOI:10.1002/wics.1461]
28. [28] Bartys, M., Patton, R., Syfert, M., Heras, S.D.L. and Quevedo, J., "Introduction to the DAMADICS actuator FDI benchmark study". Control Engineering Practice, vol.14, pp. 577-596, 2005. [DOI:10.1016/j.conengprac.2005.06.015]
29. [29] Xu, J., et al., "Performance assessment and design for univariate alarm systems based on FAR, MAR, and AAD", IEEE Transactions on Automation Science and Engineering, vol.9, no.2, pp. 296-307, 2011. [DOI:10.1109/TASE.2011.2176490]

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


© 2021 CC BY-NC 4.0 | Journal of Control

Designed & Developed by : Yektaweb