Diagnosis in industrial processes

Diagnóstico de fallos en procesos industriales

  • John William Vásquez Capacho
Palabras clave: Fault diagnosis, reliability, risk management, safety, SIS, supervision. (en_US)
Palabras clave: Diagnóstico de fallas, confiabilidad, gestión de riesgos, seguridad, SIS, supervisión (es_ES)

Resumen (en_US)

This article describes the most important aspects in the diagnosis of failures on industrial processes. An analysis of process safety is seen from monitoring tools including expert systems as well as intelligent hybrid models. The article continues to identify aspects such as reliability, risk analysis, fault diagnosis techniques and industrial control and safety systems in processes. Reliability and risk analysis provide important information in a process safety tool; analyzes such as HAZOP, FMEA, Fault trees and Bow tie are described through this article. Then compiled and summarized the different techniques and models of fault diagnosis concluding with a presentation of control and safety systems in an industrial process

Resumen (es_ES)

En este artículo, se describen los aspectos más importantes en la realización de diagnóstico de fallos en procesos industriales. Un análisis de la seguridad en procesos es visto desde las herramientas de supervisión incluyendo los sistemas expertos así como modelos híbridos inteligentes. El articulo continua identificando aspectos como la confiabilidad, análisis de riesgos, técnicas de diagnóstico de fallos y los sistemas industriales de control y seguridad en procesos. La confiabilidad y el análisis de riesgos aportan información importante en una herramienta de seguridad de procesos; análisis como HAZOP, FMEA, Fault trees y Bow tie son descritos en este artículo. Seguidamente se hace un compilado y resumen de las diferentes técnicas y modelos de diagnóstico de fallos concluyendo con una presentación de los sistemas de control  y seguridad en un proceso industrial.


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[1] A. Astolfi, L. Praly, “Global complete observability and output- to-state stability imply the existence of a globally convergent observer”. Mathematics of Control Signals and Systems, vol: 18, pp. 32-65, 2006.

[2] J. Lew, J. Juang, H. Keel, “Quantification of parametric uncertainty via an interval model”, Journal of Guidance Control and Dynamics, vol 17, no. 6, 1994, https://doi.org/10.2514/3.21335

[3] M. Bayoudh, L. Travé-Massuyès, X. Olive, “Hybrid systems diagnosability by abstracting faulty continuous dynamics”. Proc. of the 17th International Workshop on Principles of Diagnosis, pp. 915, 2006.

[4] S. Ding, “Model-based fault diagnosis techniques design schemes, algorithms, and tools”, Springer, 2008.

[5] 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, vol 30, november 2000, https://doi.org/10.1109/3468.895922

[6] R. J. Patton, J. Chen, “Observer-based fault detection and isolation: robustness and applications”, Control Engineering Practice, vol. 5, no. 5, pp. 671–682, https://doi.org/10.1016/S0967-0661(97)00049-X

[7] R. Vries, “An automated methodology for generating a fault tree”, IEEE Transactions on Reliability, 1990, https://doi.org/10.1109/24.52615

[8] F. Yang, D. Xiao, “Progress in root cause and fault propagation analysis of large scale industrial processes”, Journal of Control Science and Engineering, 2012, https://doi.org/10.1155/2012/478373

[9] H. Sarmiento, C. Isaza, “Identification and estimation of functional states in drinking water plant based on fuzzy clustering”, 22st European Symposium on Computer Aided Process Engineering. pp. 1317 to 1327, https://doi.org/10.1016/B978-0-444-59520-1.50122-6

[10] Y. Chen, J. Lee, “Autonomous mining for alarm correlation patterns based on time-shift similarity clustering in manufacturing system”. IEEE International Conference on Prognostics and Health Management, 2011.

[11] J. Vásquez, J. Prada, C. Agudelo, F. Jimenez, “Analysis of alarm man agement in startups and shutdowns for oil re ning processes”, IEEE Explorer Engineering Mechatronics and Automation International Congress, Bogota, 2013.

[12] J. Vásquez, L. Travé-Massuyès, A. Subias, F. Jimenez, C. Agudelo, “Chronicle based alarm management in startup and shutdown stages”, International Work-shop on Principles of Diagnosis, 2015.

[13] J. Vásquez, L. Travé-Massuyès, A. Subias, F. Jimenez, C. Agudelo, “Alarm management based on diagnosis”. 4th IFAC International Conference on Intelligent Control and Automation Sciences, 2016, https://doi.org/10.1016/j.ifacol.2016.07.101

[14] S. Cox, R. Tait, “Reliability, Safety, and Risk Management”. John Wiley & Sons, Ltd, 2008, https://doi.org/10.1002/9780470061572.eqr360

[15] J. Tixier, G. Dusserre, O. Salvi, D. Gaston, “Review of 62 risk analysis methodologies of industrial plants”. Journal of loss prevention in the process industries, vol 15, issue 4, 2002, https://doi.org/10.1016/S0950-4230(02)00008-6

[16] C. Wei, W. Rogers, M. Mannan, “Layer of protection analysis for reactive chemical risk assessment”, Journal of hazardous materials, vol 159, issue 1, November 2008, https://doi.org/10.1016/j.jhazmat.2008.06.105

[17] M. Sánchez, “Introducción a la con confiabilidad y evaluación de riesgos”, Bogotá: Universidad de los andes, segunda edióion, 2010.

[18] CCPS, “Guidelines for Hazard Evaluation Procedures”, New York: Wiley. Wiley, 2008.

[19] H. Devold, “Oil and Gas Production Handbook: An Introduction to Oil and Gas Production”, SRH Media, 2013.

[20] I. Fernández, A. Camacho, C. Gasco, A. Macias, M. A. Martin, G. Reyes, “Seguridad funcional en instalaciones de proceso: sistemas, instrumentados de seguridad y análisis SIL”, Ediciones Díaz de Santos, S.A, 2012.

[21] A. Shui, W. Chen, P. Zhang, S. Hu, X. Huang, “Review of fault diagnosis in control systems”, IEEE, 2009.

[22] V. Venkatasubramanian, R. Rengaswamy, K. Yin, S. N. Kavuri, “A review of process fault detection and diagnosis”, Computers and Chemical Engineering, vol 27, issue 3, 2003.

[23] A. Bittencourt, K. Saarinen, S. Sander-Tavallaey, “A data-driven method for monitoring systems that operate repetitively - applications to wear monitoring in an industrial robot joint”, 8th IFAC Symp, 2012.

[24] S. Ding, Y. Wang, S. Yin, P. Zhang, Y. Yang, E. Ding, “Date-driven design of fault-tolerant control systems”. Proc. 8th IFAC Symp, 2012.

[25] A. Shumsky, “Data driven method for fault detection and isolation in nonlinear uncertain systems”, Proc. IFAC Conf. on Control Applications in Marine Systems, 2007, https://doi.org/10.3182/20070919-3-HR-3904.00050

[26] A. Zhirabok, S. Pavlov, “Data-driven method of fault detection in technical systems”, 25th DAAAM International Symposium on Intelligent Manufacturing and Automation, 2014.

[27] P. Nomikos, J. MacGregor, “Monitoring batch processes using multiway principal component analysis”. American Institute of Chemical Engineers Journal, vol 40, issue 8, 1994.

[28] Z. Wang, C. Zhu, Z. Niu, D. Gao, X. Feng, “Monitoring batch processes using multiway principal component analysis”, Knowledge-Based Systems, 2014.

[29] V. Venkatasubramanian, R. Vaidyanathan, Y. Yamamoto, “Process fault detection and diagnosis using neural networks part i: Steady state processes”, Computers and Chemical Engineering, vol 14, issue 7, 1991.

[30] R. Rengaswamy, V. Venkatasubramanian, “A fast training neural network and its updation for incipient fault detection and diagnosis”. Computers and Chemical Engineering, vol 24, issues 2-7, 2000, https://doi.org/10.1016/S0098-1354(00)00434-8

[31] E. Garcia, C. Agudelo, F. Morant, “Secuencias de alarmas para detección y diagnóstico de fallos”, 3er Congreso internacional de ingeniería mecatrónica, 2012.

[32] S. Dash, R. Rengaswamy, V. Venkatasubramanian, “Fuzzy-logic based trend classi cation for fault diagnosis of chemical processes”. Computers & Chemical Engineering, vol 27, issue 3, 2003.

[33] M. Unal, M. Onat, M. Demetgul, H. Kucuk, “Fault diagnosis of rolling bearings using a genetic algorithm optimized neural network”. Measurement, vol 58, 2014.

[34] J. Cheung, G. Stephanopoulos, “Representation of process trends part i: A formal representation framework”, Computers & Chemical Engineering, vol 14, issue 4-5, pp. 495-510, 1990, https://doi.org/10.1016/0098-1354(90)87023-I

[35] M. Janusz, V. Venkatasubramanian, “Automatic generation of qualitative descriptions of process trends for fault detection and diagnosis”. Engineering Applications of Artificial Intelligence, vol 4, issue 5, pp. 329 – 339, 1991, https://doi.org/10.1016/0952-1976(91)90001-M

[36] R. Rengaswamy, V. Venkatasubramanian, “A syntactic pattern-recognition approach for process monitoring and fault diagnosis”. Engineering Applications of Artificial Intelligence, vol 8, issue 1, pp. 35 – 51, 1995, https://doi.org/10.1016/0952-1976(94)00058-U

[37] J. Yu, “A particle filter driven dynamic gaussian mixture model approach for complex process monitoring and fault diagnosis”, Journal of Process Control, vol 22, issue 4, 2012, https://doi.org/10.1016/j.jprocont.2012.02.012

[38] M. Basseville, “Detecting changes in signals and systems: a survey”. Automatica, vol 14, issue 3, 1988, https://doi.org/10.1016/0005-1098(88)90073-8

[39] E. Chow, A. Willsky, “Analytical redundancy and the design of robust failure detection systems”. IEEE Transactions on Automatic Control, pp.603-614, 1984, https://doi.org/10.1109/TAC.1984.1103593

[40] P. M. Frank, “Fault diagnosis in dynamic systems using analytical and knowledge- based redundancy a survey and some new results”. Automatica, vol. 26, no. 3, pp. 459–474, 1990, https://doi.org/10.1016/0005-1098(90)90018-D

[41] P. Frank, X. Ding, “Survey of robust residual generation and evaluation methods in observer-based fault detection systems”. Journal of Process Control, vol 7, issue 6, pp. 403 – 424, 1997, https://doi.org/10.1016/S0959-1524(97)00016-4

[42] J. Chen, R. Patton, “Robust model-based fault diagnosis for dynamic systems”, Massachusetts: Kluwer Academic Publishers, 1999, https://doi.org/10.1007/978-1-4615-5149-2

[43] M. Desai, A. Ray, “A fault detection and isolation methodology-theory and application”, Proceedings of American control conference, pp. 262-270, 1984.

[44] A. Willsky, “A survey of design methods for failure detection in dynamic systems”. Automatica, vol 12, issue 6, pp.601-611, 1976, https://doi.org/10.1016/0005-1098(76)90041-8

[45] M. Maurya, R. Rengaswamy, V. Venkatasubramanian, “A signed di- rected graph and qualitative trend analysis-based framework for incipient fault diagnosis”. Chemical Engineering Research and Design, vol. 85, no.10, 2007, https://doi.org/10.1016/S0263-8762(07)73181-7

[46] A. Samantaray, K. Medjaher, B. Ould-Bouamama, M. Staroswiecki, G. Dauphin- Tanguy, “Diagnostic bond graphs for online fault detection and isolation”, Simulation Modelling Practice and Theory, pp. 237 - 262, 2006, https://doi.org/10.1016/j.simpat.2005.05.003

[47] F. Yang, D. Xiao, L. Shah, “Qualitative fault detection and hazard analysis based on signed directed graphs for large-scale complex systems”, Tsinghua University, University of Alberta China, 2010.

[48] Y. Liu, G. Xie, Y. Yang, Z. Chen, Q. Chai, “Hierarchical method of fault diagnosis based on extended”, Control and Decision Conference (2014 CCDC), The 26th Chinese, pp. 3808–3812, 2014, https://doi.org/10.1109/CCDC.2014.6852843

[49] N. Wilcox, D. Himmelblau, “Possible cause and effect graphs (pceg) model for fault diagnosis i. methodology”, Computers and Chemical Engineering, vol 18, issue 2, pp.103 - 116, 1994, https://doi.org/10.1016/0098-1354(94)80131-2

[50] M. Kramer, P. L. Palowitch, “A Rule-Based Approach to Fault Diagnosis Using the Signed Directed Graph“, AIChE Journal, vol. 33, no. 7, pp. 1067 – 1078, July 1987, https://doi.org/10.1002/aic.690330703
[51] R. Vaidhyanathan, V. Venkatasubramanian, “Digraph-based models for automated hazop analysis”, Reliability Engineering and Systems Safety, vol 50, issue 1, pp.33 - 49, 1995, https://doi.org/10.1016/0951-8320(95)00052-4

[52] M. Kramer, J. B. Palowitch, “A rule-based approach to fault diagnosis using the signed directed graph”, AIChE Journal, vol 33, issue 7, pp.1067-1078, 1987, https://doi.org/10.1002/aic.690330703

[53] O. Oyeleye, M. Kramer, “Qualitative simulation of chemical process systems: steady state analysis”, AIChE Journal, vol 34, issue 9, pp. 1441-1454, 1988, https://doi.org/10.1002/aic.690340906

[54] B. Celse, S. Cauvin, B. Heim, S. Gentil, L. Travé-Massuyés, “Model based diagnostic module for a fcc pilot plant. Oil & Gas Science and Technology”, revista IFP, vol. 60, no. 4, pp. 661-67, 2005.

[55] C. Dousson, “Suivi d’é volution et reconnaissance de chroniques”. Thése de doctorat, Université Paul Sabatier, 1994.

[56] A. Subias, L. Travé-Massuyés, E. LeCorronc, “Learning chronicles signing multiple scenario instances”, FAC World Congress, 2014, https://doi.org/10.3182/20140824-6-ZA-1003.02579

[57] N. Ulerich, G. Powers, “Online hazard aversion and fault diagnosis in chemical processes: the digraph fault tree”, IEEE Transactions on Reliability, pp. 171-177, 1988,

[58] B. Kuipers, “Qualitative simulation”, Artificial Intelligence, vol 29, issue 3, pp. 289-338, 1986, https://doi.org/10.1016/0004-3702(86)90073-1

[59] J. Rasmussen, “The role of hierarchical knowledge representation in decision making and system management”. IEEE Transactions on Systems, Man and Cybernetics, pp. 234 - 243, 1985, https://doi.org/10.1109/TSMC.1985.6313353

[60] F. Finch, M. Kramer, “Narrowing diagnostic focus using functional decom- position”, American Institute of Chemical Engineers Journal, vol 34, issue 1, pp.130 - 140, 1987.
Cómo citar
Vásquez Capacho, J. W. (2017). Diagnóstico de fallos en procesos industriales. Visión electrónica, 11(2), 222-232. https://doi.org/10.14483/22484728.14621
julio-diciembre de 2017
Publicado: 2017-10-27
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