APPLICATION OF A PCA-BASED FAULT DETECTION AND DIAGNOSIS METHOD IN A POWER GENERATION SYSTEM WITH A 2 MW NATURAL GAS ENGINE

Based on increasing global energy demand, electric power generation from Internal Combustion Engines (ICE) has increased over the years. On this idea, the industries have adopted different methods and procedures to prevent failures in these engines, achieve an extension of the life cycle of the mach...

Full description

Autores:
Cardenas, Yulineth
Carrillo, Gaylord
Alviz, Anibal
Alviz, Antistio
Portnoy, Ivan
Fajardo, Juan
Ocampo, Eric
Da-Costa, Edson
Tipo de recurso:
Fecha de publicación:
2022
Institución:
Universidad Tecnológica de Bolívar
Repositorio:
Repositorio Institucional UTB
Idioma:
eng
OAI Identifier:
oai:repositorio.utb.edu.co:20.500.12585/12270
Acceso en línea:
https://hdl.handle.net/20.500.12585/12270
Palabra clave:
Batch Process;
Fault Detection;
Canonical Variate Analysis
LEMB
Rights
openAccess
License
http://creativecommons.org/licenses/by-nc-nd/4.0/
id UTB2_3718578f208198e6d272659f55db5fb2
oai_identifier_str oai:repositorio.utb.edu.co:20.500.12585/12270
network_acronym_str UTB2
network_name_str Repositorio Institucional UTB
repository_id_str
dc.title.spa.fl_str_mv APPLICATION OF A PCA-BASED FAULT DETECTION AND DIAGNOSIS METHOD IN A POWER GENERATION SYSTEM WITH A 2 MW NATURAL GAS ENGINE
title APPLICATION OF A PCA-BASED FAULT DETECTION AND DIAGNOSIS METHOD IN A POWER GENERATION SYSTEM WITH A 2 MW NATURAL GAS ENGINE
spellingShingle APPLICATION OF A PCA-BASED FAULT DETECTION AND DIAGNOSIS METHOD IN A POWER GENERATION SYSTEM WITH A 2 MW NATURAL GAS ENGINE
Batch Process;
Fault Detection;
Canonical Variate Analysis
LEMB
title_short APPLICATION OF A PCA-BASED FAULT DETECTION AND DIAGNOSIS METHOD IN A POWER GENERATION SYSTEM WITH A 2 MW NATURAL GAS ENGINE
title_full APPLICATION OF A PCA-BASED FAULT DETECTION AND DIAGNOSIS METHOD IN A POWER GENERATION SYSTEM WITH A 2 MW NATURAL GAS ENGINE
title_fullStr APPLICATION OF A PCA-BASED FAULT DETECTION AND DIAGNOSIS METHOD IN A POWER GENERATION SYSTEM WITH A 2 MW NATURAL GAS ENGINE
title_full_unstemmed APPLICATION OF A PCA-BASED FAULT DETECTION AND DIAGNOSIS METHOD IN A POWER GENERATION SYSTEM WITH A 2 MW NATURAL GAS ENGINE
title_sort APPLICATION OF A PCA-BASED FAULT DETECTION AND DIAGNOSIS METHOD IN A POWER GENERATION SYSTEM WITH A 2 MW NATURAL GAS ENGINE
dc.creator.fl_str_mv Cardenas, Yulineth
Carrillo, Gaylord
Alviz, Anibal
Alviz, Antistio
Portnoy, Ivan
Fajardo, Juan
Ocampo, Eric
Da-Costa, Edson
dc.contributor.author.none.fl_str_mv Cardenas, Yulineth
Carrillo, Gaylord
Alviz, Anibal
Alviz, Antistio
Portnoy, Ivan
Fajardo, Juan
Ocampo, Eric
Da-Costa, Edson
dc.subject.keywords.spa.fl_str_mv Batch Process;
Fault Detection;
Canonical Variate Analysis
topic Batch Process;
Fault Detection;
Canonical Variate Analysis
LEMB
dc.subject.armarc.none.fl_str_mv LEMB
description Based on increasing global energy demand, electric power generation from Internal Combustion Engines (ICE) has increased over the years. On this idea, the industries have adopted different methods and procedures to prevent failures in these engines, achieve an extension of the life cycle of the machines, improve their safety, and provide financial savings. For this reason, this work implements a methodology for detecting and identifying failures in a natural gas engine (JGS 612 GS-N. L), based on the integration of Principal Component Analysis (PCA) and alarm streak analysis. A method used to describe a data set in terms of new uncorrelated variables or components. The components are ordered by the amount of original variance they describe, making the technique useful for reducing the dimensionality of a data set. Technically, PCA searches for the projection according to which the data are best represented in terms of least squares, using the T2 and Q statistics. In the initial stage, a PCA-based algorithm was developed to detect abnormal process trends and identify the variables of greater impact when these anomalies arise. In the next stage, an algorithm was developed and implemented, based on the analysis of alarm streaks, to identify the system’s behavior and thus classify f luctuations into either normal operating condition drifts or system failures. The application of the proposed methodology with real operation data of the engine (JGS 612 GS-N. L) shows that the method outperforms operators in detecting and identifying faults, as it performs these tasks considerably earlier than operators. © The Author(s) 2022.
publishDate 2022
dc.date.issued.none.fl_str_mv 2022
dc.date.accessioned.none.fl_str_mv 2023-07-21T15:38:53Z
dc.date.available.none.fl_str_mv 2023-07-21T15:38:53Z
dc.date.submitted.none.fl_str_mv 2023
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_b1a7d7d4d402bcce
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.hasversion.spa.fl_str_mv info:eu-repo/semantics/draft
dc.type.spa.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
status_str draft
dc.identifier.citation.spa.fl_str_mv Cardenas, Y., Carrillo, G., Alviz, A., Alviz, A., Portnoy, I., Fajardo, J., Ocampo, E., & Da-Costa, E. (2022). Application of a PCA-based fault detection and diagnosis method in a power generation system with a 2 MW natural gas engine. EUREKA: Physics and Engineering, (6), 84-98. https://doi.org/10.21303/2461-4262.2022.002701
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12585/12270
dc.identifier.doi.none.fl_str_mv 10.21303/2461-4262.2022.002701
dc.identifier.instname.spa.fl_str_mv Universidad Tecnológica de Bolívar
dc.identifier.reponame.spa.fl_str_mv Repositorio Universidad Tecnológica de Bolívar
identifier_str_mv Cardenas, Y., Carrillo, G., Alviz, A., Alviz, A., Portnoy, I., Fajardo, J., Ocampo, E., & Da-Costa, E. (2022). Application of a PCA-based fault detection and diagnosis method in a power generation system with a 2 MW natural gas engine. EUREKA: Physics and Engineering, (6), 84-98. https://doi.org/10.21303/2461-4262.2022.002701
10.21303/2461-4262.2022.002701
Universidad Tecnológica de Bolívar
Repositorio Universidad Tecnológica de Bolívar
url https://hdl.handle.net/20.500.12585/12270
dc.language.iso.spa.fl_str_mv eng
language eng
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.uri.*.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.cc.*.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.extent.none.fl_str_mv 15 páginas
15 páginas
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.place.spa.fl_str_mv Cartagena de Indias
dc.source.spa.fl_str_mv EUREKA, Physics and Engineering
institution Universidad Tecnológica de Bolívar
bitstream.url.fl_str_mv https://repositorio.utb.edu.co/bitstream/20.500.12585/12270/1/2701-Article%20Text-7231-1-10-20221129.pdf
https://repositorio.utb.edu.co/bitstream/20.500.12585/12270/2/license_rdf
https://repositorio.utb.edu.co/bitstream/20.500.12585/12270/3/license.txt
https://repositorio.utb.edu.co/bitstream/20.500.12585/12270/4/2701-Article%20Text-7231-1-10-20221129.pdf.txt
https://repositorio.utb.edu.co/bitstream/20.500.12585/12270/5/2701-Article%20Text-7231-1-10-20221129.pdf.jpg
bitstream.checksum.fl_str_mv c0afd373b5eb9eb19ddb70b745ca0a6c
4460e5956bc1d1639be9ae6146a50347
e20ad307a1c5f3f25af9304a7a7c86b6
228036e8ea2e2eb9b3a71fbcb4ebe421
3984ddbe95902b28f5eea6074dcc4594
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositorio Institucional UTB
repository.mail.fl_str_mv repositorioutb@utb.edu.co
_version_ 1814021658530283520
spelling Cardenas, Yulineth5b52dc56-89d3-4d24-8b3b-21b97e29caa9Carrillo, Gaylord8f0fd8f3-9862-471f-bb58-1c8be8f60ed6Alviz, Anibalb2507fcf-32ee-472e-bde4-7d3160996aafAlviz, Antistio13d4f26a-2a97-485a-8935-29530a2ec6a9Portnoy, Ivanff8a429a-f46a-4cb5-bb6a-9b21ed7d366bFajardo, Juan5681b114-d542-428e-a5ed-8e6ceeb90db3Ocampo, Eric6b1b371a-a3a7-4928-80d4-facb634a190cDa-Costa, Edson48a22b33-9666-4901-ac9f-01b26b8e87602023-07-21T15:38:53Z2023-07-21T15:38:53Z20222023Cardenas, Y., Carrillo, G., Alviz, A., Alviz, A., Portnoy, I., Fajardo, J., Ocampo, E., & Da-Costa, E. (2022). Application of a PCA-based fault detection and diagnosis method in a power generation system with a 2 MW natural gas engine. EUREKA: Physics and Engineering, (6), 84-98. https://doi.org/10.21303/2461-4262.2022.002701https://hdl.handle.net/20.500.12585/1227010.21303/2461-4262.2022.002701Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarBased on increasing global energy demand, electric power generation from Internal Combustion Engines (ICE) has increased over the years. On this idea, the industries have adopted different methods and procedures to prevent failures in these engines, achieve an extension of the life cycle of the machines, improve their safety, and provide financial savings. For this reason, this work implements a methodology for detecting and identifying failures in a natural gas engine (JGS 612 GS-N. L), based on the integration of Principal Component Analysis (PCA) and alarm streak analysis. A method used to describe a data set in terms of new uncorrelated variables or components. The components are ordered by the amount of original variance they describe, making the technique useful for reducing the dimensionality of a data set. Technically, PCA searches for the projection according to which the data are best represented in terms of least squares, using the T2 and Q statistics. In the initial stage, a PCA-based algorithm was developed to detect abnormal process trends and identify the variables of greater impact when these anomalies arise. In the next stage, an algorithm was developed and implemented, based on the analysis of alarm streaks, to identify the system’s behavior and thus classify f luctuations into either normal operating condition drifts or system failures. The application of the proposed methodology with real operation data of the engine (JGS 612 GS-N. L) shows that the method outperforms operators in detecting and identifying faults, as it performs these tasks considerably earlier than operators. © The Author(s) 2022.15 páginas15 páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2EUREKA, Physics and EngineeringAPPLICATION OF A PCA-BASED FAULT DETECTION AND DIAGNOSIS METHOD IN A POWER GENERATION SYSTEM WITH A 2 MW NATURAL GAS ENGINEinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/drafthttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/resource_type/c_2df8fbb1Batch Process;Fault Detection;Canonical Variate AnalysisLEMBCartagena de Indiasvan Schrick, D. Remarks on Terminology in the Field of Supervision, Fault Detection and Diagnosis (1997) IFAC Proceedings Volumes, 30 (18), pp. 959-964. Cited 47 times. https://doi.org/10.1016/s1474-6670(17)42524-9Quiñones-Grueiro, M., Prieto-Moreno, A., Verde, C., Llanes-Santiago, O. Data-driven monitoring of multimode continuous processes: A review (2019) Chemometrics and Intelligent Laboratory Systems, 189, pp. 56-71. Cited 90 times. www.elsevier.com/locate/chemometrics doi: 10.1016/j.chemolab.2019.03.012Coussement, A., Gicquel, O., Parente, A. MG-local-PCA method for reduced order combustion modeling (2013) Proceedings of the Combustion Institute, 34 (1), pp. 1117-1123. Cited 39 times. http://www.sciencedirect.com/science/journal/15407489 doi: 10.1016/j.proci.2012.05.073Jung, D., Ng, K.Y., Frisk, E., Krysander, M. Combining model-based diagnosis and data-driven anomaly classifiers for fault isolation (2018) Control Engineering Practice, 80, pp. 146-156. Cited 46 times. www.elsevier.com/inca/publications/store/1/2/3/ doi: 10.1016/j.conengprac.2018.08.013Haanchumpol, T., Sudasna-na-Ayudthya, P., Singhtaun, C. Modern multivariate control chart using spatial signed rank for non-normal process (2020) Engineering Science and Technology, an International Journal, 23 (4), pp. 859-869. Cited 8 times. www.journals.elsevier.com/engineering-science-and-technology-an-international-journal/ doi: 10.1016/j.jestch.2019.12.001Isermann, R. Model-based fault-detection and diagnosis - Status and applications (2005) Annual Reviews in Control, 29 (1), pp. 71-85. Cited 1329 times. doi: 10.1016/j.arcontrol.2004.12.002Jafarian, K., Mobin, M., Jafari-Marandi, R., Rabiei, E. Misfire and valve clearance faults detection in the combustion engines based on a multi-sensor vibration signal monitoring (2018) Measurement: Journal of the International Measurement Confederation, 128, pp. 527-536. Cited 77 times. doi: 10.1016/j.measurement.2018.04.062Portnoy, I., Melendez, K., Pinzon, H., Sanjuan, M. An improved weighted recursive PCA algorithm for adaptive fault detection (2016) Control Engineering Practice, 50, pp. 69-83. Cited 82 times. www.elsevier.com/inca/publications/store/1/2/3/ doi: 10.1016/j.conengprac.2016.02.010Niu, G., Xiong, L., Qin, X., Pecht, M. Fault detection isolation and diagnosis of multi-axle speed sensors for high-speed trains (2019) Mechanical Systems and Signal Processing, 131, pp. 183-198. Cited 26 times. http://www.elsevier.com/inca/publications/store/6/2/2/9/1/2/index.htt doi: 10.1016/j.ymssp.2019.05.053Albarbar, A., Gu, F., Ball, A.D. Diesel engine fuel injection monitoring using acoustic measurements and independent component analysis (2010) Measurement: Journal of the International Measurement Confederation, 43 (10), pp. 1376-1386. Cited 114 times. doi: 10.1016/j.measurement.2010.08.003Shahnazari, H. Fault diagnosis of nonlinear systems using recurrent neural networks (Open Access) (2020) Chemical Engineering Research and Design, 153, pp. 233-245. Cited 31 times. http://www.elsevier.com/wps/find/journaldescription.cws_home/713871/description#description doi: 10.1016/j.cherd.2019.09.026Ahmadi, H., Gholamzadeh, M., Shahmoradi, L., Nilashi, M., Rashvand, P. Diseases diagnosis using fuzzy logic methods: A systematic and meta-analysis review (Open Access) (2018) Computer Methods and Programs in Biomedicine, 161, pp. 145-172. Cited 103 times. www.elsevier.com/locate/cmpb doi: 10.1016/j.cmpb.2018.04.013Cardenas, Y. (2019) Fallas en bujías para motores de generación a gas. Cited 2 times. (Tesis de maestría). Universidad del AtanticoCamacho, J., Pérez-Villegas, A., Garciá-Teodoro, P., MacIá-Fernández, G. PCA-based multivariate statistical network monitoring for anomaly detection (2016) Computers and Security, 59, pp. 118-137. Cited 83 times. doi: 10.1016/j.cose.2016.02.008Meglen, R.R. Examining large databases: a chemometric approach using principal component analysis (1992) Marine Chemistry, 39 (1-3), pp. 217-237. Cited 191 times. doi: 10.1016/0304-4203(92)90103-HAversano, G., Parra-Alvarez, J.C., Isaac, B.J., Smith, S.T., Coussement, A., Gicquel, O., Parente, A. PCA and Kriging for the efficient exploration of consistency regions in Uncertainty Quantification (2019) Proceedings of the Combustion Institute, 37 (4), pp. 4461-4469. Cited 12 times. http://www.sciencedirect.com/science/journal/15407489 doi: 10.1016/j.proci.2018.07.040Li, Z., Yan, X., Yuan, C., Peng, Z., Li, L. Virtual prototype and experimental research on gear multi-fault diagnosis using wavelet-autoregressive model and principal component analysis method (Open Access) (2011) Mechanical Systems and Signal Processing, 25 (7), pp. 2589-2607. Cited 142 times. doi: 10.1016/j.ymssp.2011.02.017Rosković, A., Grbić, R., Slišković, D. Fault tolerant system in a process measurement system based on the PCA method (2011) MIPRO 2011 - 34th International Convention on Information and Communication Technology, Electronics and Microelectronics - Proceedings, art. no. 5967325, pp. 1646-1651. Cited 4 times. ISBN: 978-953233067-0Harrou, F., Nounou, M., Nounou, H. A statistical fault detection strategy using PCA based EWMA control schemes (2013) 2013 9th Asian Control Conference, ASCC 2013, art. no. 6606311. Cited 16 times. ISBN: 978-146735769-2 doi: 10.1109/ASCC.2013.6606311Ding, S., Zhang, P., Ding, E., Yin, S., Naik, A., Deng, P., Gui, W. On the Application of PCA Technique to Fault Diagnosis (2010) Tsinghua Science and Technology, 15 (2), pp. 138-144. Cited 130 times. doi: 10.1016/S1007-0214(10)70043-2Yin, S., Ding, S.X., Naik, A., Deng, P., Haghani, A. On PCA-based fault diagnosis techniques (2010) Conference on Control and Fault-Tolerant Systems, SysTol'10 - Final Program and Book of Abstracts, art. no. 5676031, pp. 179-184. Cited 31 times. ISBN: 978-142448154-5 doi: 10.1109/SYSTOL.2010.5676031Tong, C., Lan, T., Shi, X. Fault detection and diagnosis of dynamic processes using weighted dynamic decentralized PCA approach (2017) Chemometrics and Intelligent Laboratory Systems, 161, pp. 34-42. Cited 57 times. www.elsevier.com/locate/chemometrics doi: 10.1016/j.chemolab.2016.11.015Hu, Z., Chen, Z., Gui, W., Jiang, B. Adaptive PCA based fault diagnosis scheme in imperial smelting process (Open Access) (2014) ISA Transactions, 53 (5), pp. 1446-1455. Cited 59 times. http://www.elsevier.com/wps/find/journaldescription.cws_home/524244/description#description doi: 10.1016/j.isatra.2013.12.018Huang, Y., Shen, L., Liu, H. Grey relational analysis, principal component analysis and forecasting of carbon emissions based on long short-term memory in China (2019) Journal of Cleaner Production, 209, pp. 415-423. Cited 147 times. https://www.journals.elsevier.com/journal-of-cleaner-production doi: 10.1016/j.jclepro.2018.10.128Miller, P., Swanson, R. E., Heckler, C. E. Contribution plots: A missing link in multivariate quality control (1998) Applied mathematics and computer science, 8 (4), pp. 775-792. Cited 346 times.Oliveira, J.C.M., Pontes, K.V., Sartori, I., Embiruçu, M. Fault Detection and Diagnosis in dynamic systems using Weightless Neural Networks (2017) Expert Systems with Applications, 84, pp. 200-219. Cited 36 times. doi: 10.1016/j.eswa.2017.05.020Mårtensson, J., Hjalmarsson, H. Variance-error quantification for identified poles and zeros (2009) Automatica, 45 (11), pp. 2512-2525. Cited 16 times. doi: 10.1016/j.automatica.2009.08.001Wu, X. Study on mean-standard deviation shortest path problem in stochastic and time-dependent networks: A stochastic dominance based approach (2015) Transportation Research Part B: Methodological, 80, pp. 275-290. Cited 28 times. www.elsevier.com/inca/publications/store/5/4/8/ doi: 10.1016/j.trb.2015.07.009Boutellaa, E., Kerdjidj, O., Ghanem, K. Covariance matrix based fall detection from multiple wearable sensors (2019) Journal of Biomedical Informatics, 94, art. no. 103189. Cited 30 times. http://www.elsevier.com/inca/publications/store/6/2/2/8/5/7/index.htt doi: 10.1016/j.jbi.2019.103189Yang, H., Li, S., Li, K. Order estimation of multivariable ill-conditioned processes based on PCA method (Open Access) (2012) Journal of Process Control, 22 (7), pp. 1397-1403. Cited 6 times. doi: 10.1016/j.jprocont.2012.06.013Zumoffen, D. (2008) Desarrollo de Sistemas de Diagnóstico de Fallas Integrado al Diseño de Control Tolerante a Fallas en Procesos Químicos. Cited 3 times.Lane, S., Martin, E.B., Morris, A.J., Gower, P. Application of exponentially weighted principal component analysis for the monitoring of a polymer film manufacturing process (2003) Transactions of the Institute of Measurement and Control, 25 (1), pp. 17-35. Cited 53 times. https://journals.sagepub.com/home/TIM doi: 10.1191/0142331203tm071oaVenkatasubramanian, V., Rengaswamy, R., Kavuri, S.N., Yin, K. A review of process fault detection and diagnosis part III: Process history based methods (2003) Computers and Chemical Engineering, 27 (3), pp. 327-346. Cited 1520 times. doi: 10.1016/S0098-1354(02)00162-Xhttp://purl.org/coar/resource_type/c_6501ORIGINAL2701-Article Text-7231-1-10-20221129.pdf2701-Article Text-7231-1-10-20221129.pdfapplication/pdf5035728https://repositorio.utb.edu.co/bitstream/20.500.12585/12270/1/2701-Article%20Text-7231-1-10-20221129.pdfc0afd373b5eb9eb19ddb70b745ca0a6cMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.utb.edu.co/bitstream/20.500.12585/12270/2/license_rdf4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83182https://repositorio.utb.edu.co/bitstream/20.500.12585/12270/3/license.txte20ad307a1c5f3f25af9304a7a7c86b6MD53TEXT2701-Article Text-7231-1-10-20221129.pdf.txt2701-Article Text-7231-1-10-20221129.pdf.txtExtracted texttext/plain39573https://repositorio.utb.edu.co/bitstream/20.500.12585/12270/4/2701-Article%20Text-7231-1-10-20221129.pdf.txt228036e8ea2e2eb9b3a71fbcb4ebe421MD54THUMBNAIL2701-Article Text-7231-1-10-20221129.pdf.jpg2701-Article Text-7231-1-10-20221129.pdf.jpgGenerated Thumbnailimage/jpeg5512https://repositorio.utb.edu.co/bitstream/20.500.12585/12270/5/2701-Article%20Text-7231-1-10-20221129.pdf.jpg3984ddbe95902b28f5eea6074dcc4594MD5520.500.12585/12270oai:repositorio.utb.edu.co:20.500.12585/122702023-07-22 00:17:50.691Repositorio Institucional UTBrepositorioutb@utb.edu.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

Publicaciones similares