An additional layer of protection through superalarms with diagnosis capability

An alarm management methodology can be proposed as a discrete event sequence recognition problem where time patterns are used to identify the process safe condition, especially in the start-up and shutdown stages. Industrial plants, particularly in the petrochemical, energy, and chemical sectors, re...

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Autores:: Vásquez-Capacho, John-William
Perez-Zuñiga, Gustavo
Muñoz, Yecid
Ospino, Adalberto

Tipo de recurso:: Article of journal

Fecha de publicación:: 2020

Institución:: Corporación Universidad de la Costa

Repositorio:: REDICUC - Repositorio CUC

Idioma:: eng

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dc.title.spa.fl_str_mv	An additional layer of protection through superalarms with diagnosis capability
dc.title.translated.spa.fl_str_mv	Una nueva capa de protección a través de súper alarmas con capacidad de diagnóstico
title	An additional layer of protection through superalarms with diagnosis capability
spellingShingle	An additional layer of protection through superalarms with diagnosis capability Alarm management Protection layers Safe process Diagnosis Super-alarm Gestión de alarmas Capas de protección Procesos de seguridad Diagnóstico Super-alarma
title_short	An additional layer of protection through superalarms with diagnosis capability
title_full	An additional layer of protection through superalarms with diagnosis capability
title_fullStr	An additional layer of protection through superalarms with diagnosis capability
title_full_unstemmed	An additional layer of protection through superalarms with diagnosis capability
title_sort	An additional layer of protection through superalarms with diagnosis capability
dc.creator.fl_str_mv	Vásquez-Capacho, John-William Perez-Zuñiga, Gustavo Muñoz, Yecid Ospino, Adalberto
dc.contributor.author.spa.fl_str_mv	Vásquez-Capacho, John-William Perez-Zuñiga, Gustavo Muñoz, Yecid Ospino, Adalberto
dc.subject.spa.fl_str_mv	Alarm management Protection layers Safe process Diagnosis Super-alarm Gestión de alarmas Capas de protección Procesos de seguridad Diagnóstico Super-alarma
topic	Alarm management Protection layers Safe process Diagnosis Super-alarm Gestión de alarmas Capas de protección Procesos de seguridad Diagnóstico Super-alarma
description	An alarm management methodology can be proposed as a discrete event sequence recognition problem where time patterns are used to identify the process safe condition, especially in the start-up and shutdown stages. Industrial plants, particularly in the petrochemical, energy, and chemical sectors, require a combined approach of all the events that can result in a catastrophic accident. This document introduces a new layer of protection (super-alarm) for industrial processes based on a diagnostic stage. Alarms and actions of the standard operating procedure are considered discrete events involved in sequences, where the diagnostic stage corresponds to the recognition of a special situation when these sequences occur. This is meant to provide operators with pertinent information regarding the normal or abnormal situations induced by the flow of alarms. Chronicles Based Alarm Management (CBAM) is the methodology used to build the chronicles that will permit to generate the super-alarms furthermore, a case study of the petrochemical sector using CBAM is presented to build the chronicles of the normal startup, abnormal start-up, and normal shutdown scenarios. Finally, the scenario validation is performed for an abnormal start-up, showing how a super-alarm is generated.
publishDate	2020
dc.date.accessioned.none.fl_str_mv	2020-07-17T15:15:37Z
dc.date.available.none.fl_str_mv	2020-07-17T15:15:37Z
dc.date.issued.none.fl_str_mv	2020-03-17
dc.type.spa.fl_str_mv	Artículo de revista
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dc.identifier.issn.spa.fl_str_mv	0122-5383
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dc.identifier.doi.spa.fl_str_mv	DOI : 10.29047/01225383.168
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identifier_str_mv	0122-5383 DOI : 10.29047/01225383.168 Corporación Universidad de la Costa REDICUC - Repositorio CUC
url	https://hdl.handle.net/11323/6622 https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv	eng
language	eng
dc.relation.references.spa.fl_str_mv	[1] R. W. Brennan, Toward real-time distributed intelligent control: A survey of research themes and applications, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 37 (5) (2007) 744–765. doi:10.1109/TSMCC.2007.900670. [2] M. Khalgui, O. Mosbahi, Z. Li, H. Hanisch, Reconfigura tion of distributed embedded-control systems, IEEE/ ASME Transactions on Mechatronics 16 (4) (2011) 684–694. doi:10.1109/TMECH.2010.2050697. [3] D. J. Reifer, Software failure modes and effects analysis, IEEE Transactions on Reliability R-28 (3) (1979) 247–249. doi:10.1109/TR.1979.5220578. [4] M. G. Mehrabi, A. G. Ulsoy, Y. Koren, Reconfigurable manufacturing systems: Key to future manufacturing, Journal of Intelligent Manufacturing 11 (4) (2000) 403–419. doi:10.1023/A:1008930403506. URL https://doi.org/10.1023/A:1008930403506 [5] V. Rodrigo, M. Chioua, T. Hagglund, M. Hollender, Causal analysis for alarm flood reduction, IFACPapersOnLine 49 (7) (2016) 723 – 728, 11th IFAC Symposium on Dynamics and Control of Process SystemsIncluding Biosystems DYCOPS-CAB 2016. doi:https://doi.org/10.1016/j.ifacol.2016.07.269. [6] L. Bodsberg, P. Hokstad, Alarm and shutdown frequencies in offshore production, IFAC Proceedings Volumes 21 (15) (1988) 19 – 25, Ifac Workshop on Industrial Process Control Systems, Bruges, Belgium, 28 30 September. doi:https://doi.org/10.1016/S14746670(17)54672-8. [7] C. Agudelo, F. Morant Anglada, E. Quiles Cucarella, E. Garca Moreno, Secuencias de alarmas para detecci´on y diagno´stico de fallos, Revista Colombiana de Computaci´on 12 (2) (2011) 31–44. doi:10.29375/25392115.1798. [8] C. Agudelo, Integracio´n de t´ecnicas y las secuencias de alarmas para la deteccio´n y el diagnostico de fallos, Ph.D. thesis, Universidad Politecnica de Valencia (2016). doi:doi:10.4995/Thesis/10251/63450. URL https://riunet. upv.es/handle/10251/63450 [9] J. W. Vásquez Capacho, Chronicle Based Alarm Management, These, INSA Toulouse, these en cotutelle avec l’Universidad de los Andes, Colombie (Oct. 2017). URL https://hal.laas.fr/tel-02059631 [10] L. Magni, R. Scattolini, C. Rossi, A fault detection and isolation method for complex industrial systems, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 30 (6) (2000) 860–865. doi:10.1109/3468.895922. [11] M. Hollender, T. Skovholt, J. Evans, Holistic alarm management throughout the plant lifecycle, in: 2016 Petroleum and Chemical Industry Conference Europe (PCIC Europe), 2016, pp. 1–6. doi:10.1109/ PCICEurope.2016.7604645. [12] R. Patton, J. Chen, Observer-based fault detection and isolation: Robustness and applications, Control Engineering Practice 5 (5) (1997) 671 – 682. doi:https:// doi.org/10.1016/S0967-0661(97)00049-X. [13] R. 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Chen, Quantification of alarm chatter based on run length distributions, Chemical Engineering Research and Design 91 (12) (2013) 2550 – 2558. doi:https://doi. org/10.1016/j.cherd.2013.02.028. [24] P. Urban, L. Landryov, Identification and evaluation of alarm logs from the alarm management system, in: 2016 17th International Carpathian Control Conference (ICCC), 2016, pp. 769–774. doi:10.1109/ CarpathianCC.2016.7501199. [25] S. D. Treville, J. Antonakis, N. M. Edelson, Can standard operating procedures be motivating? reconciling process variability issues and behavioural outcomes, Total Quality Management & Business Excellence 16 (2) (2005) 231– 241. arXiv:https://doi.org/10.1080/14783360500054236, doi: 10.1080/14783360500054236. URL https://doi. org/10.1080/14783360500054236 [26] S. Sklet, Safety barriers: Definition, classification, and performance, Journal of Loss Prevention in the Process Industries 19 (5) (2006) 494 – 506. doi:https:// doi.org/10.1016/j.jlp.2005.12.004. [27] A. M. Dowell III, Layer of protection analysis and inherently safer processes, Process Safety Progress 18 (4) 214–220. arXiv:https://aiche.onlinelibrary.wiley. com/doi/pdf/10.1002/prs.680180409, doi:10.1002/ prs.680180409. [28] J. M. Koscielny, M. Bartys, The requirements for a new layer in the industrial safety systems, IFACPapersOnLine 48 (21) (2015) 1333 – 1338, 9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2015. doi:https:// doi.org/10.1016/j.ifacol.2015.09.710. [29] R. Isermann, Model-based fault-detection and diagnosis status and applications, Annual Reviews in Control 29 (1) (2005) 71 – 85. doi:https://doi.org/10.1016/j. arcontrol.2004.12.002. [30] R. Isermann, On the applicability of model-based fault detection for technical processes, Control Engineering Practice 2 (3) (1994) 439 – 450. doi:https:// doi.org/10.1016/0967-0661(94)90781-1. [31] M. Bayoudh, L. Trav´e-Massuy`es, X. Olive, Hybrid systems diagnosis by coupling continuous and discrete event techniques, IFAC Proceedings Volumes 41 (2) (2008) 7265 – 7270, 17th IFAC World Congress. doi:https://doi.org/10.3182/20080706-5-KR-1001.01229. [32] Z. Gao, C. Cecati, S. X. Ding, A survey of fault diagnosis and fault tolerant techniquespart i: Fault diagnosis with model-based and signal based approaches, IEEE Transactions on Industrial Electronics 62 (6) (2015) 3757–3767. doi:10.1109/TIE.2015.2417501. [33] A. Subias, L. Travé-Massuyès, E. L. Corronc, Learning chronicles signing multiple scenario instances, IFAC Proceedings Volumes 47 (3) (2014) 10397 – 10402, 19th IFAC World Congress. doi:https://doi. org/10.3182/20140824-6-ZA-1003.02579. [34] D. Beebe, S. Ferrer, D. Logerot, The connection of peak alarm rates to plant incidents and what you can do to minimize, Process Safety Progress 32 (1) 72–77. arXiv:https://aiche.onlinelibrary.wiley.com/doi/ pdf/10.1002/prs.11539, doi:10.1002/prs.11539. [35] J. Zhu, Y. 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A combined anal-ysis of plant connectivity and alarm logs to reduce the number of alerts in an automation system. Journal of Process Control, Vol 23, pp. 839–851. [43]Sandeep, R., Kondaveeti, R., Izadi, I., Shaha, S. L., Black, T., and Chen, T. (2014). Graphical tools for routine assessment of industrial alarm systems. Computers & Chemical Engineering,Vol 46, pp. 39–47. [44] Higuchi, F., Yamamoto, I., T. Takai, M. N., and Nishitani, H. (2014). Use of event correlation analysis to reduce number of alarms. Computers & Chemical Engineering, Vol 27, pp. 1521–1526. [45] Ge, Z. and Song, Z. (2009). Multimode process monitoring based on Bayesian method. Journal of Chemometrics, Vol 23, pp. 636–650. [46]Liu, X., Noda, M., and Nishitani, H. (2010). Evaluation of plant alarm systems by behavior simulation using a virtual subject. Computers & Chemical Engineering, Vol 34, pp. 374–386. [47]Yang, F., Shah, S. L., Xiao, D., and Chen, T. (2011). Improved correlation analysis and visualization of industrial alarm data. 18th IFAC World Congress Milano, Italy. [48]Izadi, I., S.L. Shah, a. D. S., Kondaveeti, S., and Chen, T. (2009). A framework for optimal design of alarm systems. 7th IFAC Symposium on fault detection, supervision and safety of technical processes, Barcelona, Spain. [49]Pariyani, A., Seider, W., Oktem, U., and Soroush, M. (2012). Dynamic risk analysis using alarm databases to improve process safety and product quality: Part ii bayesian analysis. AIChE Journal, Vol. 58, pp. 826–841. [50]Liu, J. and Chen, D. (2010). Non stationary fault detection and diagnosis for multimode processes. AIChE Journal, Vol. 56, pp. 207–219. [51]Zhu, J., Shu, Y., Zhao, J., and Yang, F. (2013). A dynamic alarm management strategy for chemical process transitions. Journal of Loss Prevention in the Process Industries. [52]Jing, Z., Boang, L., and Hao, Y. (2013). Fault diagnosis strategy for startup process based on standard operating procedures. 25th Chinese Control and Decision Conference (CCDC). [53]Duan, P., Yang, F., Chen, T., and Shah., S. (2013). Direct causality detection via the transfer entropy approach. IEEE Transactions of control system technology, Vol. 21, No. 6. [54]Yang, F. and Xiao, D. (2012). Progress in root cause and fault propagation analysis of large scale industrial processes. Journal of Control Science and Engineering. [55]Srinivasan, R., Viswanathan, P., Vedam, H., and Nochur, A. (2005). A framework for managing transitions in chemical plants. Computers & Chemical Engineering, Vol. 29, pp. 305–322. [56]Xu, S., Adhitya, A., and Srinivasan, R. (2013). Hybrid model-based framework for alarm anticipation. Industrial & Engineering Chemistry Research, Vol. 53, pp. 5182–5193
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spelling	Vásquez-Capacho, John-Williamfc745de554dcda9f8d64c3738b385d47Perez-Zuñiga, Gustavo2b268fb28f5770cb68a660fda1a82446Muñoz, Yecidfbcc92f1bb643b473bb86c70bf4d36dfOspino, Adalberto0f6133f29b5e6676c3d8da117b2ca4432020-07-17T15:15:37Z2020-07-17T15:15:37Z2020-03-170122-5383https://hdl.handle.net/11323/6622DOI : 10.29047/01225383.168Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/An alarm management methodology can be proposed as a discrete event sequence recognition problem where time patterns are used to identify the process safe condition, especially in the start-up and shutdown stages. Industrial plants, particularly in the petrochemical, energy, and chemical sectors, require a combined approach of all the events that can result in a catastrophic accident. This document introduces a new layer of protection (super-alarm) for industrial processes based on a diagnostic stage. Alarms and actions of the standard operating procedure are considered discrete events involved in sequences, where the diagnostic stage corresponds to the recognition of a special situation when these sequences occur. This is meant to provide operators with pertinent information regarding the normal or abnormal situations induced by the flow of alarms. Chronicles Based Alarm Management (CBAM) is the methodology used to build the chronicles that will permit to generate the super-alarms furthermore, a case study of the petrochemical sector using CBAM is presented to build the chronicles of the normal startup, abnormal start-up, and normal shutdown scenarios. Finally, the scenario validation is performed for an abnormal start-up, showing how a super-alarm is generated.Se puede formular una metodología de gestión de alarmas como un problema de reconocimiento de secuencia de eventos discretos en el que se utilizan patrones de tiempo para identificar la condición segura del proceso, especialmente en las etapas de arranque y parada de planta. Las plantas industriales, particularmente en las industrias petroquímica, energética y química, requieren una administración combinada de todos los eventos que pueden producir un accidente catastrófico. En este documento, se introduce una nueva capa de protección (súper alarma) a los procesos industriales basados en una etapa de diagnóstico. Las alarmas y las acciones estándar del procedimiento operativo son asumidas como eventos discretos involucrados en las secuencias, luego la etapa de diagnóstico corresponde al reconocimiento de la situación cuando ocurren estas secuencias. Esto proporciona a los operadores información pertinente sobre las situaciones normales o anormales inducidas por el flujo de alarmas. La gestión de alarmas basadas en crónicas (CBAM) es la metodología utilizada en este artículo para construir las crónicas que permitirán generar las super alarmas, además, se presenta un caso de estudio del sector petroquímico que usa CBAM para construir las crónicas de los escenarios de un arranque normal, un arranque anormal y un apagado normal. Finalmente, la validación del escenario se realiza para un arranque anormal, mostrando cómo se genera una súper alarma.engCT y F - Ciencia, Tecnologia y FuturoCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Alarm managementProtection layersSafe processDiagnosisSuper-alarmGestión de alarmasCapas de protecciónProcesos de seguridadDiagnósticoSuper-alarmaAn additional layer of protection through superalarms with diagnosis capabilityUna nueva capa de protección a través de súper alarmas con capacidad de diagnósticoArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[1] R. W. Brennan, Toward real-time distributed intelligent control: A survey of research themes and applications, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 37 (5) (2007) 744–765. doi:10.1109/TSMCC.2007.900670.[2] M. Khalgui, O. Mosbahi, Z. Li, H. Hanisch, Reconfigura tion of distributed embedded-control systems, IEEE/ ASME Transactions on Mechatronics 16 (4) (2011) 684–694. doi:10.1109/TMECH.2010.2050697.[3] D. J. Reifer, Software failure modes and effects analysis, IEEE Transactions on Reliability R-28 (3) (1979) 247–249. doi:10.1109/TR.1979.5220578.[4] M. G. Mehrabi, A. G. Ulsoy, Y. Koren, Reconfigurable manufacturing systems: Key to future manufacturing, Journal of Intelligent Manufacturing 11 (4) (2000) 403–419. doi:10.1023/A:1008930403506. URL https://doi.org/10.1023/A:1008930403506[5] V. Rodrigo, M. Chioua, T. Hagglund, M. Hollender, Causal analysis for alarm flood reduction, IFACPapersOnLine 49 (7) (2016) 723 – 728, 11th IFAC Symposium on Dynamics and Control of Process SystemsIncluding Biosystems DYCOPS-CAB 2016. doi:https://doi.org/10.1016/j.ifacol.2016.07.269.[6] L. Bodsberg, P. Hokstad, Alarm and shutdown frequencies in offshore production, IFAC Proceedings Volumes 21 (15) (1988) 19 – 25, Ifac Workshop on Industrial Process Control Systems, Bruges, Belgium, 28 30 September. doi:https://doi.org/10.1016/S14746670(17)54672-8.[7] C. Agudelo, F. Morant Anglada, E. Quiles Cucarella, E. Garca Moreno, Secuencias de alarmas para detecci´on y diagno´stico de fallos, Revista Colombiana de Computaci´on 12 (2) (2011) 31–44. doi:10.29375/25392115.1798.[8] C. Agudelo, Integracio´n de t´ecnicas y las secuencias de alarmas para la deteccio´n y el diagnostico de fallos, Ph.D. thesis, Universidad Politecnica de Valencia (2016). doi:doi:10.4995/Thesis/10251/63450. URL https://riunet. upv.es/handle/10251/63450[9] J. W. 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Industrial & Engineering Chemistry Research, Vol. 53, pp. 5182–5193ORIGINALAn additional layer of protection through superalarms with diagnosis capability.pdfAn additional layer of protection through superalarms with diagnosis capability.pdfapplication/pdf2149123https://repositorio.cuc.edu.co/bitstream/11323/6622/1/An%20additional%20layer%20of%20protection%20through%20superalarms%20with%20diagnosis%20capability.pdf356e6db91f07382c560d0e6606f5defdMD51open accessCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstream/11323/6622/2/license_rdf42fd4ad1e89814f5e4a476b409eb708cMD52open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstream/11323/6622/3/license.txte30e9215131d99561d40d6b0abbe9badMD53open accessTHUMBNAILAn additional layer of protection through superalarms with diagnosis capability.pdf.jpgAn additional layer of protection through superalarms with diagnosis 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An additional layer of protection through superalarms with diagnosis capability

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