Kalman filters for leak diagnosis in pipelines: brief history and future research

The purpose of this paper is to provide a structural review of the progress made on the detection and localization of leaks in pipelines by using approaches based on the Kalman filter. To the best of the author’s knowledge, this is the first review on the topic. In particular, it is the first to try to...

Full description

Autores:
Torres, Lizeth
Jiménez-Cabas, Javier
González, Omar
Molina, Lázaro
Lopez Estrada, Francisco Ronay
Tipo de recurso:
Article of journal
Fecha de publicación:
2020
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/6226
Acceso en línea:
https://hdl.handle.net/11323/6226
https://repositorio.cuc.edu.co/
Palabra clave:
Leak detection
Kalman filter
Pipelines
Rights
openAccess
License
CC0 1.0 Universal
id RCUC2_6c5e5401056e80447f49e5e1c1e9d46d
oai_identifier_str oai:repositorio.cuc.edu.co:11323/6226
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Kalman filters for leak diagnosis in pipelines: brief history and future research
title Kalman filters for leak diagnosis in pipelines: brief history and future research
spellingShingle Kalman filters for leak diagnosis in pipelines: brief history and future research
Leak detection
Kalman filter
Pipelines
title_short Kalman filters for leak diagnosis in pipelines: brief history and future research
title_full Kalman filters for leak diagnosis in pipelines: brief history and future research
title_fullStr Kalman filters for leak diagnosis in pipelines: brief history and future research
title_full_unstemmed Kalman filters for leak diagnosis in pipelines: brief history and future research
title_sort Kalman filters for leak diagnosis in pipelines: brief history and future research
dc.creator.fl_str_mv Torres, Lizeth
Jiménez-Cabas, Javier
González, Omar
Molina, Lázaro
Lopez Estrada, Francisco Ronay
dc.contributor.author.spa.fl_str_mv Torres, Lizeth
Jiménez-Cabas, Javier
González, Omar
Molina, Lázaro
Lopez Estrada, Francisco Ronay
dc.subject.spa.fl_str_mv Leak detection
Kalman filter
Pipelines
topic Leak detection
Kalman filter
Pipelines
description The purpose of this paper is to provide a structural review of the progress made on the detection and localization of leaks in pipelines by using approaches based on the Kalman filter. To the best of the author’s knowledge, this is the first review on the topic. In particular, it is the first to try to draw the attention of the leak detection community to the important contributions that use the Kalman filter as the core of a computational pipeline monitoring system. Without being exhaustive, the paper gathers the results from different research groups such that these are presented in a unified fashion. For this reason, a classification of the current approaches based on the Kalman filter is proposed. For each of the existing approaches within this classification, the basic concepts, theoretical results, and relations with the other procedures are discussed in detail. The review starts with a short summary of essential ideas about state observers. Then, a brief history of the use of the Kalman filter for diagnosing leaks is described by mentioning the most outstanding approaches. At last, brief discussions of some emerging research problems, such as the leak detection in pipelines transporting heavy oils; the main challenges; and some open issues are addressed.
publishDate 2020
dc.date.accessioned.none.fl_str_mv 2020-04-20T21:58:30Z
dc.date.available.none.fl_str_mv 2020-04-20T21:58:30Z
dc.date.issued.none.fl_str_mv 2020-03-05
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/ART
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
format http://purl.org/coar/resource_type/c_6501
status_str acceptedVersion
dc.identifier.issn.spa.fl_str_mv 2077-1312
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/6226
dc.identifier.doi.spa.fl_str_mv doi:10.3390/jmse8030173
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 2077-1312
doi:10.3390/jmse8030173
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/6226
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv 1. Drumond, G.P.; Pasqualino, I.P.; Pinheiro, B.C.; Estefen, S.F. Pipelines, risers and umbilicals failures: A literature review. Ocean Eng. 2018, 148, 412–425. [CrossRef]
2. Bermúdez, J.R.; López-Estrada, F.R.; Besançon, G.; Valencia-Palomo, G.; Torres, L.; Hernández, H.R. Modeling and simulation of a hydraulic network for leak diagnosis. Math. Comput. Appl. 2018, 23, 70. [CrossRef]
3. American Petroleum Institute. API RP 1130 (2007): Computational Pipeline Monitoring for Liquids; American Petroleum Institute: Washington, DC, USA, September 2007.
4. Geiger, G.; Werner, T.; Matko, D. Leak detection and locating-a survey. In Proceedings of the PSIG Annual Meeting, Pipeline Simulation Interest Group, Bern, Switzerland, 15–17 October 2003.
5. Besançon, G. Nonlinear Observers and Applications; Springer: Berlin, Germany, 2007; p. 224.
6. Besançon, G. Observer tools for pipeline monitoring. In Modeling and Monitoring of Pipelines and Networks; Springer: Berlin, Germany, 2017; pp. 83–97.
7. Recent Advances in Pipeline Monitoring and Oil Leakage Detection Technologies: Principles and Approaches. Sensors 2019, 19, 2548. [CrossRef] [PubMed]
8. Simon, D. Kalman filtering with state constraints: a survey of linear and nonlinear algorithms. IET Control Theor. Appl. 2010, 4, 1303–1318. [CrossRef]
9. Evensen, G. Data Assimilation: The Ensemble Kalman Filter; Springer: New York, NY, USA, 2009.
10. Digernes, T. Real-Time Failure-Detection and Identification Applied to Supervision of Oil Transport in Pipelines. Model. Identif. Control 1980, 1, 39–49. [CrossRef]
11. Benkherouf, A.; Allidina, A. Leak detection and location in gas pipelines. IEE Proc. D-Control Theory Appl. 1988, 135, 142–148. [CrossRef]
12. Besançon, G.; Georges, D.; Begovich, O.; Verde, C.; Aldana, C. Direct observer design for leak detection and estimation in pipelines. In Proceedings of the 2007 European Control Conference (ECC), Kos, Greece, 2–5 July 2007; pp. 5666–5670.
13. Geiger, I.G. Principles of leak detection. In Fundamentals of Leak Detection; KROHNE Oil and Gas: Reken, Germany, 2005.
14. Emara-Shabaik, H.; Khulief, Y.; Hussaini, I. A non-linear multiple-model state estimation scheme for pipeline leak detection and isolation. Proc. Instit. Mech. Eng. Part I J. Syst. Control Eng. 2002, 216, 497–512. [CrossRef]
15. Khulief, Y.; Emara-Shabaik, H. Laboratory investigation of a multiple-model state estimation scheme for detection and isolation of leaks in pipelines. Proc. Instit. Mech. Eng. Part I J. Syst. Control Eng. 2006, 220, 1–13. [CrossRef]
16. Bai, L.; Yue, Q.; Li, H. Sub-sea Pipelines Leak Detection and Location Based on Fluid Transient and FDI. In Proceedings of the the Fourteenth International Offshore and Polar Engineering Conference, Toulon, France, 23–28 May 2004.
17. Doney, K. Leak Detection in Pipelines Using the Extended Kalman Filter and the Extended Boundary Approach. Ph.D. Thesis, University of Saskatchewan, Saskatoon, SK, Canada, 10 October 2007.
18. Begovich, O.; Navarro, A.; Sanchez, E.N.; Besançon, G. Comparison of two detection algorithms for pipeline leaks. In Proceedings of the IEEE International Conference on Control Applications, CCA 2007, Singapore, 1–3 October 2007; pp. 777–782.
19. Billmann, L.; Isermann, R. Leak detection methods for pipelines. Automatica 1987, 23, 381–385. [CrossRef]
20. Verde, C. Multi-leak detection and isolation in fluid pipelines. Control Eng. Pract. 2001, 9, 673–682. [CrossRef]
21. Chaudhry, M.H. Applied Hydraulic Transients; Van Nostrand Reinhold: New York, NY, USA, 1979.
22. Verde, C.; Bornard, G.; Gentil, S. Isolability of multi-leaks in a pipeline. In Proceedings of the 4th MATHMOD, Vienna, Austria, 5–7 February 2003.
23. Torres, L.; Besançon, G.; Georges, D. A collocation model for water-hammer dynamics with application to leak detection. In Proceedings of the 47th IEEE Conference on Decision and Control, Cancún, Mexico, 9–11 December 2008; pp. 3890–3894.
24. Torres, L.; Besançon, G.; Georges, D. Collocation modeling with experimental validation for pipeline dynamics and application to transient data estimations. In Proceedings of the European Control Conference, ECC’09, Budapest, Hungary, 23–26 August 2009.
25. Dos Santos, P.L.; Azevedo-Perdicoúlis, T.; Ramos, J.; Jank, G.; de Carvalho, J.M.; Milhinhos, J. Gas pipelines LPV modelling and identification for leakage detection. In Proceedings of the American Control Conference (ACC2010), Baltimore, Maryland, USA, 30 June–2 July 2010; pp. 1211–1216.
26. Navarro, A.; Begovich, O.; Besançon, G.; Dulhoste, J. Real-time leak isolation based on state estimation in a plastic pipeline. In Proceedings of the IEEE International Conference on Control Applications CCA 2011 , Denver, CO, USA, 28–30 September 2011; pp. 953–957.
27. Colombo, A.F.; Karney, B.W. Energy and costs of leaky pipes: toward comprehensive picture. J. Water Resour. Plan. Manag. 2002, 128, 441–450. [CrossRef]
28. Delgado-Aguiñaga, J.; Begovich, O.; Besançon, G. Varying-parameter modeling and extended Kalman filtering for reliable leak diagnosis under temperature variations. In Proceedings of the 20th International Conference on System Theory, Control and Computing (ICSTCC), Sinaia, Romania, 13–15 October 2016 ; pp. 632–637.
29. Verde, C.; Torres, L.; González, O. Decentralized scheme for leaks’ location in a branched pipeline. J. Loss Prev. Process Ind. 2016, 43, 18–28. [CrossRef]
30. Delgado-Aguiñaga, J.A.; Begovich, O. Water Leak Diagnosis in Pressurized Pipelines: A Real Case Study. In Modeling and Monitoring of Pipelines and Networks; Springer: New York, NY, USA, 2017; pp. 235–262.
31. Navarro, A.; Begovich, O.; Sánchez, J.; Besançon, G. Real-Time Leak Isolation Based on State Estimation with Fitting Loss Coefficient Calibration in a Plastic Pipeline. Asian J. Control 2017, 19, 255–265. [CrossRef]
32. Santos-Ruiz, I.; Bermúdez, J.; López-Estrada, F.; Puig, V.; Torres, L.; Delgado-Aguiñaga, J. Online leak diagnosis in pipelines using an EKF-based and steady-state mixed approach. Control Eng. Pract. 2018, 81, 55–64. [CrossRef]
33. Delgado-Aguiñaga, J.; Besançon, G. EKF-based leak diagnosis schemes for pipeline networks. IFAC-PapersOnLine 2018, 51, 723–729. [CrossRef]
34. Liu, P.; Li, S.; Wang, Z. Multi-leak diagnosis and isolation in oil pipelines based on Unscented Kalman filter. In Proceedings of the 30th Chinese Control And Decision Conference (CCDC), Shenyang, China, 9–11 June 2018; pp. 2222–2227.
35. Verde, C. Minimal order nonlinear observer for leak detection. J. Dyn. Syst. Meas. Control 2004, 126, 467–472. [CrossRef]
36. Verde, C.; Visairo, N. Identificability of multi-leaks in a pipeline. In Proceedings of the American Control Conference (ACC), Boston, MA, USA, 30 June–2 July 2004; Volume 5, pp. 4378–4383.
37. Gong, J.; Cai, J.; Li, X.; Song, S. Research on state estimation of oil pipeline considering adaptive extended Kalman filtering. In Proceedings of the 2007 International Conference on Mechatronics and Automation, Harbin, China, 5–8 August 2007; pp. 1294–1298.
38. Yu, Z.; Jian, L.; Zhoumo, Z.; Jin, S. A combined Kalman filter-Discrete wavelet transform method for leakage detection of crude oil pipelines. In Proceedings of the 9th International Conference on Electronic Measurement & Instruments, Beijing, China, 16–19 August 2009; pp. 3–1086.
39. Vianna, F.; Orlande, H.; Dulikravich, G. Estimation of the temperature field in pipelines by using the Kalman filter. In Proceedings of the 2nd International Congress of Serbian Society of Mechanics (IConSSM 2009) , Palic (Subotica), Serbia, 1–5 June 2009; pp. 1 – 5.
40. Ye, G.; Fenner, R.A. Kalman filtering of hydraulic measurements for burst detection in water distribution systems. J. Pipeline Syst. Eng. Pract. 2010, 2, 14–22. [CrossRef]
41. Dos Santos, P.L.; Azevedo-Perdicoúlis, T.; Jank, G.; Ramos, J.; de Carvalho, J.M. Leakage detection and location in gas pipelines through an LPV identification approach. Commun. Nonlinear Sci. Numer. Simul. 2011, 16, 4657–4665. [CrossRef]
42. Navarro, A.; Begovich, O.; Besançon, G. Calibration of fitting loss coefficients for modelling purpose of a plastic pipeline. In Proceedings of the IEEE 16th Conference on Emerging Technologies & Factory Automation (ETFA), Toulouse, France, 5–9 September 2011; pp. 1 – 6.
43. Pizano-Moreno, A.; Begovich, O. Leak isolation with temperature compensation in pipelines. In Proceedings of the 9th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), Mexico City, Mexico, 26–28 September 2012; pp. 1 – 5.
44. Padilla, E.A.; Begovich, O. Real-time leak isolation based on a fault model approach algorithm in a water pipeline prototype. IFAC Proc. Vol. 2012, 45, 916–921. [CrossRef]
45. Modisette, J.P. State estimation of pipeline models using the ensemble Kalman filter. In Proceedings of the PSIG Annual Meeting, Pipeline Simulation Interest Group, Prague, Czech Republic, 16–19 April 2013.
46. Guillen, M.; Dulhoste, J.F.; Besançon, G.; Scola, I.R.; Santos, R.; Georges, D. Leak detection and location based on improved pipe model and nonlinear observer. In Proceedings of the European Control Conference (ECC2014), Strasbourg, France, 24–27 June 2014; pp. 958–963.
47. Tian, J.; Wang, S.Q. Design and realization of the Kalman filter based on LabVIEW. Applied Mechanics and Materials. Trans Tech. Publ. 2014, 519, 1276–1280.
48. Okeya, I.; Kapelan, Z.; Hutton, C.; Naga, D. Online burst detection in a water distribution system using the Kalman filter and hydraulic modelling. Procedia Eng. 2014, 89, 418–427. [CrossRef]
49. Behrooz, H.A.; Boozarjomehry, R.B. Modeling and state estimation for gas transmission networks. J. Nat. Gas Sci. Eng. 2015, 22, 551–570. [CrossRef]
50. Al Ghailani, L.; El-Sinawi, A. Dynamic model of a new above-ground pipeline using a Kalman estimator-based system. In Proceedings of the IEEE Conference on Systems, Process and Control ( ICSPC), Kuala Lumpur, Malaysia, 19–20 December 2015; pp. 12–15.
51. Zhou, D. Research on Natural Gas Pipeline Leak Detection Algorithm and Simulation. In Proceedings of the 2015 Chinese Intelligent Automation Conference, Fuzhou, China, 2–19 June 2015; pp. 355–361.
52. Verde, C.; Rojas, J. Iterative Scheme for Sequential Leaks Location. IFAC-PapersOnLine 2015, 48, 726–731. [CrossRef]
53. Choi, D.Y.; Kim, S.W.; Choi, M.A.; Geem, Z.W. Adaptive Kalman filter based on adjustable sampling interval in burst detection for water distribution system. Water 2016, 8, 142. [CrossRef]
54. Durgut, I.;˙ Leblebiciog˘lu, M.K. State estimation of transient flow in gas pipelines by a Kalman filter-based estimator. J. Nat. Gas Sci. Eng. 2016, 35, 189–196. [CrossRef]
55. Delgado-Aguiñaga, J.; Besançon, G.; Begovich, O.; Carvajal, J. Multi-leak diagnosis in pipelines based on Extended Kalman Filter. Control Eng. Pract. 2016, 49, 139–148. [CrossRef]
56. Brunone, B. Transient test-based technique for leak detection in outfall pipes. J. Water Resour. Plan. Manag. 1999, 125, 302–306. [CrossRef]
57. Kalman, R.E. A new approach to linear filtering and prediction Problems. J. Basic Eng. 1960, 82, 35–45. [CrossRef]
58. Lauritzen, S.L. Time series analysis in 1880: A discussion of contributions made by TN Thiele. Int. Stat. Rev./Revue Int. Stat. 1981, 49, 319–331. [CrossRef]
59. Lauritzen, S.L. Thiele: Pioneer in Statistics; Clarendon Press: Oxford, UK, 2002.
60. Ljung, L. Asymptotic behavior of the extended Kalman filter as a parameter estimator for linear systems. IEEE Trans. Autom. Control 1979, 24, 36–50. [CrossRef]
61. Song, Y.; Grizzle, J.W. The extended Kalman filter as a local asymptotic observer for nonlinear discrete-time systems. In Proceedings of the American Control Conference, Chicago, IL, USA, 24–26 June 1992 ; pp. 3365–3369.
62. Andrews, A. Kalman Filtering: Theory and Practice Using MATLAB; Wiley: New York, NY, USA, January 2001. 63. Isermann, R.; Münchhof, M. Identification of Dynamic Systems: An Introduction with Applications; Springer: New York, NY, USA, 2010.
dc.rights.spa.fl_str_mv CC0 1.0 Universal
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/publicdomain/zero/1.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.coar.spa.fl_str_mv http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv CC0 1.0 Universal
http://creativecommons.org/publicdomain/zero/1.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.publisher.spa.fl_str_mv Journal of Marine Science and Engineering
institution Corporación Universidad de la Costa
bitstream.url.fl_str_mv https://repositorio.cuc.edu.co/bitstream/11323/6226/1/Kalman%20Filters%20for%20Leak%20Diagnosis%20in%20Pipelines.%20Brief%20History%20and%20Future%20Research.pdf
https://repositorio.cuc.edu.co/bitstream/11323/6226/2/license_rdf
https://repositorio.cuc.edu.co/bitstream/11323/6226/3/license.txt
https://repositorio.cuc.edu.co/bitstream/11323/6226/4/Kalman%20Filters%20for%20Leak%20Diagnosis%20in%20Pipelines.%20Brief%20History%20and%20Future%20Research.pdf.jpg
https://repositorio.cuc.edu.co/bitstream/11323/6226/5/Kalman%20Filters%20for%20Leak%20Diagnosis%20in%20Pipelines.%20Brief%20History%20and%20Future%20Research.pdf.txt
bitstream.checksum.fl_str_mv 56f5dee27bb50dd0c9a9d181d911c673
42fd4ad1e89814f5e4a476b409eb708c
8a4605be74aa9ea9d79846c1fba20a33
e5a3888a3f08009567d378d035480238
4a5768dfd2f1c45a5aba1c20b17d4d46
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositorio Universidad de La Costa
repository.mail.fl_str_mv bdigital@metabiblioteca.com
_version_ 1808400088263819264
spelling Torres, Lizethcd076d7acbc9394032fadec6d03c56acJiménez-Cabas, Javierab65972567cfb7a5fa089d3aa18ed292González, Omar221c7d1b57a36bf3beaa753c4b03865aMolina, Lázaro886f5e9bb6b07c9ae1817b1fc9c57f3d300Lopez Estrada, Francisco Ronay55a9e55a9e7d77ae03d33216684a20362020-04-20T21:58:30Z2020-04-20T21:58:30Z2020-03-052077-1312https://hdl.handle.net/11323/6226doi:10.3390/jmse8030173Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The purpose of this paper is to provide a structural review of the progress made on the detection and localization of leaks in pipelines by using approaches based on the Kalman filter. To the best of the author’s knowledge, this is the first review on the topic. In particular, it is the first to try to draw the attention of the leak detection community to the important contributions that use the Kalman filter as the core of a computational pipeline monitoring system. Without being exhaustive, the paper gathers the results from different research groups such that these are presented in a unified fashion. For this reason, a classification of the current approaches based on the Kalman filter is proposed. For each of the existing approaches within this classification, the basic concepts, theoretical results, and relations with the other procedures are discussed in detail. The review starts with a short summary of essential ideas about state observers. Then, a brief history of the use of the Kalman filter for diagnosing leaks is described by mentioning the most outstanding approaches. At last, brief discussions of some emerging research problems, such as the leak detection in pipelines transporting heavy oils; the main challenges; and some open issues are addressed.engJournal of Marine Science and EngineeringCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Leak detectionKalman filterPipelinesKalman filters for leak diagnosis in pipelines: brief history and future researchArtículo 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/acceptedVersion1. Drumond, G.P.; Pasqualino, I.P.; Pinheiro, B.C.; Estefen, S.F. Pipelines, risers and umbilicals failures: A literature review. Ocean Eng. 2018, 148, 412–425. [CrossRef]2. Bermúdez, J.R.; López-Estrada, F.R.; Besançon, G.; Valencia-Palomo, G.; Torres, L.; Hernández, H.R. Modeling and simulation of a hydraulic network for leak diagnosis. Math. Comput. Appl. 2018, 23, 70. [CrossRef]3. American Petroleum Institute. API RP 1130 (2007): Computational Pipeline Monitoring for Liquids; American Petroleum Institute: Washington, DC, USA, September 2007.4. Geiger, G.; Werner, T.; Matko, D. Leak detection and locating-a survey. In Proceedings of the PSIG Annual Meeting, Pipeline Simulation Interest Group, Bern, Switzerland, 15–17 October 2003.5. Besançon, G. Nonlinear Observers and Applications; Springer: Berlin, Germany, 2007; p. 224.6. Besançon, G. Observer tools for pipeline monitoring. In Modeling and Monitoring of Pipelines and Networks; Springer: Berlin, Germany, 2017; pp. 83–97.7. Recent Advances in Pipeline Monitoring and Oil Leakage Detection Technologies: Principles and Approaches. Sensors 2019, 19, 2548. [CrossRef] [PubMed]8. Simon, D. Kalman filtering with state constraints: a survey of linear and nonlinear algorithms. IET Control Theor. Appl. 2010, 4, 1303–1318. [CrossRef]9. Evensen, G. Data Assimilation: The Ensemble Kalman Filter; Springer: New York, NY, USA, 2009.10. Digernes, T. Real-Time Failure-Detection and Identification Applied to Supervision of Oil Transport in Pipelines. Model. Identif. Control 1980, 1, 39–49. [CrossRef]11. Benkherouf, A.; Allidina, A. Leak detection and location in gas pipelines. IEE Proc. D-Control Theory Appl. 1988, 135, 142–148. [CrossRef]12. Besançon, G.; Georges, D.; Begovich, O.; Verde, C.; Aldana, C. Direct observer design for leak detection and estimation in pipelines. In Proceedings of the 2007 European Control Conference (ECC), Kos, Greece, 2–5 July 2007; pp. 5666–5670.13. Geiger, I.G. Principles of leak detection. In Fundamentals of Leak Detection; KROHNE Oil and Gas: Reken, Germany, 2005.14. Emara-Shabaik, H.; Khulief, Y.; Hussaini, I. A non-linear multiple-model state estimation scheme for pipeline leak detection and isolation. Proc. Instit. Mech. Eng. Part I J. Syst. Control Eng. 2002, 216, 497–512. [CrossRef]15. Khulief, Y.; Emara-Shabaik, H. Laboratory investigation of a multiple-model state estimation scheme for detection and isolation of leaks in pipelines. Proc. Instit. Mech. Eng. Part I J. Syst. Control Eng. 2006, 220, 1–13. [CrossRef]16. Bai, L.; Yue, Q.; Li, H. Sub-sea Pipelines Leak Detection and Location Based on Fluid Transient and FDI. In Proceedings of the the Fourteenth International Offshore and Polar Engineering Conference, Toulon, France, 23–28 May 2004.17. Doney, K. Leak Detection in Pipelines Using the Extended Kalman Filter and the Extended Boundary Approach. Ph.D. Thesis, University of Saskatchewan, Saskatoon, SK, Canada, 10 October 2007.18. Begovich, O.; Navarro, A.; Sanchez, E.N.; Besançon, G. Comparison of two detection algorithms for pipeline leaks. In Proceedings of the IEEE International Conference on Control Applications, CCA 2007, Singapore, 1–3 October 2007; pp. 777–782.19. Billmann, L.; Isermann, R. Leak detection methods for pipelines. Automatica 1987, 23, 381–385. [CrossRef]20. Verde, C. Multi-leak detection and isolation in fluid pipelines. Control Eng. Pract. 2001, 9, 673–682. [CrossRef]21. Chaudhry, M.H. Applied Hydraulic Transients; Van Nostrand Reinhold: New York, NY, USA, 1979.22. Verde, C.; Bornard, G.; Gentil, S. Isolability of multi-leaks in a pipeline. In Proceedings of the 4th MATHMOD, Vienna, Austria, 5–7 February 2003.23. Torres, L.; Besançon, G.; Georges, D. A collocation model for water-hammer dynamics with application to leak detection. In Proceedings of the 47th IEEE Conference on Decision and Control, Cancún, Mexico, 9–11 December 2008; pp. 3890–3894.24. Torres, L.; Besançon, G.; Georges, D. Collocation modeling with experimental validation for pipeline dynamics and application to transient data estimations. In Proceedings of the European Control Conference, ECC’09, Budapest, Hungary, 23–26 August 2009.25. Dos Santos, P.L.; Azevedo-Perdicoúlis, T.; Ramos, J.; Jank, G.; de Carvalho, J.M.; Milhinhos, J. Gas pipelines LPV modelling and identification for leakage detection. In Proceedings of the American Control Conference (ACC2010), Baltimore, Maryland, USA, 30 June–2 July 2010; pp. 1211–1216.26. Navarro, A.; Begovich, O.; Besançon, G.; Dulhoste, J. Real-time leak isolation based on state estimation in a plastic pipeline. In Proceedings of the IEEE International Conference on Control Applications CCA 2011 , Denver, CO, USA, 28–30 September 2011; pp. 953–957.27. Colombo, A.F.; Karney, B.W. Energy and costs of leaky pipes: toward comprehensive picture. J. Water Resour. Plan. Manag. 2002, 128, 441–450. [CrossRef]28. Delgado-Aguiñaga, J.; Begovich, O.; Besançon, G. Varying-parameter modeling and extended Kalman filtering for reliable leak diagnosis under temperature variations. In Proceedings of the 20th International Conference on System Theory, Control and Computing (ICSTCC), Sinaia, Romania, 13–15 October 2016 ; pp. 632–637.29. Verde, C.; Torres, L.; González, O. Decentralized scheme for leaks’ location in a branched pipeline. J. Loss Prev. Process Ind. 2016, 43, 18–28. [CrossRef]30. Delgado-Aguiñaga, J.A.; Begovich, O. Water Leak Diagnosis in Pressurized Pipelines: A Real Case Study. In Modeling and Monitoring of Pipelines and Networks; Springer: New York, NY, USA, 2017; pp. 235–262.31. Navarro, A.; Begovich, O.; Sánchez, J.; Besançon, G. Real-Time Leak Isolation Based on State Estimation with Fitting Loss Coefficient Calibration in a Plastic Pipeline. Asian J. Control 2017, 19, 255–265. [CrossRef]32. Santos-Ruiz, I.; Bermúdez, J.; López-Estrada, F.; Puig, V.; Torres, L.; Delgado-Aguiñaga, J. Online leak diagnosis in pipelines using an EKF-based and steady-state mixed approach. Control Eng. Pract. 2018, 81, 55–64. [CrossRef]33. Delgado-Aguiñaga, J.; Besançon, G. EKF-based leak diagnosis schemes for pipeline networks. IFAC-PapersOnLine 2018, 51, 723–729. [CrossRef]34. Liu, P.; Li, S.; Wang, Z. Multi-leak diagnosis and isolation in oil pipelines based on Unscented Kalman filter. In Proceedings of the 30th Chinese Control And Decision Conference (CCDC), Shenyang, China, 9–11 June 2018; pp. 2222–2227.35. Verde, C. Minimal order nonlinear observer for leak detection. J. Dyn. Syst. Meas. Control 2004, 126, 467–472. [CrossRef]36. Verde, C.; Visairo, N. Identificability of multi-leaks in a pipeline. In Proceedings of the American Control Conference (ACC), Boston, MA, USA, 30 June–2 July 2004; Volume 5, pp. 4378–4383.37. Gong, J.; Cai, J.; Li, X.; Song, S. Research on state estimation of oil pipeline considering adaptive extended Kalman filtering. In Proceedings of the 2007 International Conference on Mechatronics and Automation, Harbin, China, 5–8 August 2007; pp. 1294–1298.38. Yu, Z.; Jian, L.; Zhoumo, Z.; Jin, S. A combined Kalman filter-Discrete wavelet transform method for leakage detection of crude oil pipelines. In Proceedings of the 9th International Conference on Electronic Measurement & Instruments, Beijing, China, 16–19 August 2009; pp. 3–1086.39. Vianna, F.; Orlande, H.; Dulikravich, G. Estimation of the temperature field in pipelines by using the Kalman filter. In Proceedings of the 2nd International Congress of Serbian Society of Mechanics (IConSSM 2009) , Palic (Subotica), Serbia, 1–5 June 2009; pp. 1 – 5.40. Ye, G.; Fenner, R.A. Kalman filtering of hydraulic measurements for burst detection in water distribution systems. J. Pipeline Syst. Eng. Pract. 2010, 2, 14–22. [CrossRef]41. Dos Santos, P.L.; Azevedo-Perdicoúlis, T.; Jank, G.; Ramos, J.; de Carvalho, J.M. Leakage detection and location in gas pipelines through an LPV identification approach. Commun. Nonlinear Sci. Numer. Simul. 2011, 16, 4657–4665. [CrossRef]42. Navarro, A.; Begovich, O.; Besançon, G. Calibration of fitting loss coefficients for modelling purpose of a plastic pipeline. In Proceedings of the IEEE 16th Conference on Emerging Technologies & Factory Automation (ETFA), Toulouse, France, 5–9 September 2011; pp. 1 – 6.43. Pizano-Moreno, A.; Begovich, O. Leak isolation with temperature compensation in pipelines. In Proceedings of the 9th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), Mexico City, Mexico, 26–28 September 2012; pp. 1 – 5.44. Padilla, E.A.; Begovich, O. Real-time leak isolation based on a fault model approach algorithm in a water pipeline prototype. IFAC Proc. Vol. 2012, 45, 916–921. [CrossRef]45. Modisette, J.P. State estimation of pipeline models using the ensemble Kalman filter. In Proceedings of the PSIG Annual Meeting, Pipeline Simulation Interest Group, Prague, Czech Republic, 16–19 April 2013.46. Guillen, M.; Dulhoste, J.F.; Besançon, G.; Scola, I.R.; Santos, R.; Georges, D. Leak detection and location based on improved pipe model and nonlinear observer. In Proceedings of the European Control Conference (ECC2014), Strasbourg, France, 24–27 June 2014; pp. 958–963.47. Tian, J.; Wang, S.Q. Design and realization of the Kalman filter based on LabVIEW. Applied Mechanics and Materials. Trans Tech. Publ. 2014, 519, 1276–1280.48. Okeya, I.; Kapelan, Z.; Hutton, C.; Naga, D. Online burst detection in a water distribution system using the Kalman filter and hydraulic modelling. Procedia Eng. 2014, 89, 418–427. [CrossRef]49. Behrooz, H.A.; Boozarjomehry, R.B. Modeling and state estimation for gas transmission networks. J. Nat. Gas Sci. Eng. 2015, 22, 551–570. [CrossRef]50. Al Ghailani, L.; El-Sinawi, A. Dynamic model of a new above-ground pipeline using a Kalman estimator-based system. In Proceedings of the IEEE Conference on Systems, Process and Control ( ICSPC), Kuala Lumpur, Malaysia, 19–20 December 2015; pp. 12–15.51. Zhou, D. Research on Natural Gas Pipeline Leak Detection Algorithm and Simulation. In Proceedings of the 2015 Chinese Intelligent Automation Conference, Fuzhou, China, 2–19 June 2015; pp. 355–361.52. Verde, C.; Rojas, J. Iterative Scheme for Sequential Leaks Location. IFAC-PapersOnLine 2015, 48, 726–731. [CrossRef]53. Choi, D.Y.; Kim, S.W.; Choi, M.A.; Geem, Z.W. Adaptive Kalman filter based on adjustable sampling interval in burst detection for water distribution system. Water 2016, 8, 142. [CrossRef]54. Durgut, I.;˙ Leblebiciog˘lu, M.K. State estimation of transient flow in gas pipelines by a Kalman filter-based estimator. J. Nat. Gas Sci. Eng. 2016, 35, 189–196. [CrossRef]55. Delgado-Aguiñaga, J.; Besançon, G.; Begovich, O.; Carvajal, J. Multi-leak diagnosis in pipelines based on Extended Kalman Filter. Control Eng. Pract. 2016, 49, 139–148. [CrossRef]56. Brunone, B. Transient test-based technique for leak detection in outfall pipes. J. Water Resour. Plan. Manag. 1999, 125, 302–306. [CrossRef]57. Kalman, R.E. A new approach to linear filtering and prediction Problems. J. Basic Eng. 1960, 82, 35–45. [CrossRef]58. Lauritzen, S.L. Time series analysis in 1880: A discussion of contributions made by TN Thiele. Int. Stat. Rev./Revue Int. Stat. 1981, 49, 319–331. [CrossRef]59. Lauritzen, S.L. Thiele: Pioneer in Statistics; Clarendon Press: Oxford, UK, 2002.60. Ljung, L. Asymptotic behavior of the extended Kalman filter as a parameter estimator for linear systems. IEEE Trans. Autom. Control 1979, 24, 36–50. [CrossRef]61. Song, Y.; Grizzle, J.W. The extended Kalman filter as a local asymptotic observer for nonlinear discrete-time systems. In Proceedings of the American Control Conference, Chicago, IL, USA, 24–26 June 1992 ; pp. 3365–3369.62. Andrews, A. Kalman Filtering: Theory and Practice Using MATLAB; Wiley: New York, NY, USA, January 2001. 63. Isermann, R.; Münchhof, M. Identification of Dynamic Systems: An Introduction with Applications; Springer: New York, NY, USA, 2010.ORIGINALKalman Filters for Leak Diagnosis in Pipelines. Brief History and Future Research.pdfKalman Filters for Leak Diagnosis in Pipelines. Brief History and Future Research.pdfapplication/pdf2193308https://repositorio.cuc.edu.co/bitstream/11323/6226/1/Kalman%20Filters%20for%20Leak%20Diagnosis%20in%20Pipelines.%20Brief%20History%20and%20Future%20Research.pdf56f5dee27bb50dd0c9a9d181d911c673MD51open accessCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstream/11323/6226/2/license_rdf42fd4ad1e89814f5e4a476b409eb708cMD52open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.cuc.edu.co/bitstream/11323/6226/3/license.txt8a4605be74aa9ea9d79846c1fba20a33MD53open accessTHUMBNAILKalman Filters for Leak Diagnosis in Pipelines. Brief History and Future Research.pdf.jpgKalman Filters for Leak Diagnosis in Pipelines. Brief History and Future Research.pdf.jpgimage/jpeg67368https://repositorio.cuc.edu.co/bitstream/11323/6226/4/Kalman%20Filters%20for%20Leak%20Diagnosis%20in%20Pipelines.%20Brief%20History%20and%20Future%20Research.pdf.jpge5a3888a3f08009567d378d035480238MD54open accessTEXTKalman Filters for Leak Diagnosis in Pipelines. Brief History and Future Research.pdf.txtKalman Filters for Leak Diagnosis in Pipelines. Brief History and Future Research.pdf.txttext/plain62905https://repositorio.cuc.edu.co/bitstream/11323/6226/5/Kalman%20Filters%20for%20Leak%20Diagnosis%20in%20Pipelines.%20Brief%20History%20and%20Future%20Research.pdf.txt4a5768dfd2f1c45a5aba1c20b17d4d46MD55open access11323/6226oai:repositorio.cuc.edu.co:11323/62262023-12-14 14:09:34.194CC0 1.0 Universal|||http://creativecommons.org/publicdomain/zero/1.0/open accessRepositorio Universidad de La Costabdigital@metabiblioteca.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

Publicaciones similares