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...

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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/
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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
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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
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dc.rights.cc.*.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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eu_rights_str_mv openAccess
dc.format.extent.none.fl_str_mv 15 páginas
15 páginas
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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
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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. 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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. 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