Variable control tool in MATLAB for energy transformation processes
During the stages of transformation of energy in a process, exercise control over the variables that intervene in it, improve its performance, and identify undesirable conditions in these. Thus, this study is developed as a graphical interface to implement a methodology for controlling variables of...
- Autores:
-
Cardenas, Y
Carrillo, G E
Alviz, A
Carrillo, G
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/10035
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/10035
https://iopscience.iop.org/article/10.1088/1742-6596/1708/1/012035
- Palabra clave:
- Data handling
Energy conversion
Engines
MATLAB
Matrix algebra
Multivariant analysis
Statistics
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Variable control tool in MATLAB for energy transformation processes |
| title |
Variable control tool in MATLAB for energy transformation processes |
| spellingShingle |
Variable control tool in MATLAB for energy transformation processes Data handling Energy conversion Engines MATLAB Matrix algebra Multivariant analysis Statistics LEMB |
| title_short |
Variable control tool in MATLAB for energy transformation processes |
| title_full |
Variable control tool in MATLAB for energy transformation processes |
| title_fullStr |
Variable control tool in MATLAB for energy transformation processes |
| title_full_unstemmed |
Variable control tool in MATLAB for energy transformation processes |
| title_sort |
Variable control tool in MATLAB for energy transformation processes |
| dc.creator.fl_str_mv |
Cardenas, Y Carrillo, G E Alviz, A Carrillo, G |
| dc.contributor.author.none.fl_str_mv |
Cardenas, Y Carrillo, G E Alviz, A Carrillo, G |
| dc.subject.keywords.es_CO.fl_str_mv |
Data handling Energy conversion Engines MATLAB Matrix algebra Multivariant analysis Statistics |
| topic |
Data handling Energy conversion Engines MATLAB Matrix algebra Multivariant analysis Statistics LEMB |
| dc.subject.armarc.none.fl_str_mv |
LEMB |
| description |
During the stages of transformation of energy in a process, exercise control over the variables that intervene in it, improve its performance, and identify undesirable conditions in these. Thus, this study is developed as a graphical interface to implement a methodology for controlling variables of energy conversion processes, such as internal combustion engines. The control tool developed in MATLAB variables is based on multivariate statistics. The methods for developing this tool of Graphic User Interface is based on the statistics of principal component analysis and failure statistics such as T2 Hotelling and the Q statistic that allows the control of anomalies presented in the operation's behavior. About the methodology, first, the input data are normalized, achieving standardization of the observation matrix vs. variables, then the spectral decomposition of the normalized data is performed, reaching the generation of the matrix of auto-values, allowing the age of the projection space of the data. With this based and delimited, it is possible to establish the ranges of observation of the mentioned statisticians. The result obtained from this research corresponds to software that allows the constant observation and analysis of the behavior of each variable of the generation engine. It describes the upper limit, lower limit, arithmetic mean, principal components, graphics of the statistics, and detects the failures in real times. |
| publishDate |
2020 |
| dc.date.issued.none.fl_str_mv |
2020-10 |
| dc.date.accessioned.none.fl_str_mv |
2021-02-17T20:33:32Z |
| dc.date.available.none.fl_str_mv |
2021-02-17T20:33:32Z |
| dc.date.submitted.none.fl_str_mv |
2021-02-15 |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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info:eu-repo/semantics/lecture |
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http://purl.org/coar/resource_type/c_8544 |
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publishedVersion |
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Y Cardenas et al 2020 J. Phys.: Conf. Ser. 1708 012035 |
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https://hdl.handle.net/20.500.12585/10035 |
| dc.identifier.url.none.fl_str_mv |
https://iopscience.iop.org/article/10.1088/1742-6596/1708/1/012035 |
| dc.identifier.doi.none.fl_str_mv |
10.1088/1742-6596/1708/1/012035 |
| dc.identifier.instname.es_CO.fl_str_mv |
Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
| identifier_str_mv |
Y Cardenas et al 2020 J. Phys.: Conf. Ser. 1708 012035 10.1088/1742-6596/1708/1/012035 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
| url |
https://hdl.handle.net/20.500.12585/10035 https://iopscience.iop.org/article/10.1088/1742-6596/1708/1/012035 |
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eng |
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eng |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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info:eu-repo/semantics/openAccess |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://purl.org/coar/access_right/c_abf2 |
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openAccess |
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8 páginas |
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application/pdf |
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Cartagena de Indias |
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Journal of Physics: Conference Series, Volume 1708 |
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Universidad Tecnológica de Bolívar |
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Cardenas, Y3b4627a5-81f7-462b-97c3-acb9af43408eCarrillo, G Ed1de93af-aa79-478f-9d48-0f302581d99fAlviz, Afb38fec4-6d03-488d-8cac-a3ca08b71552Carrillo, Ga1e8c876-c03d-48dd-a0bd-1fabfc1444932021-02-17T20:33:32Z2021-02-17T20:33:32Z2020-102021-02-15Y Cardenas et al 2020 J. Phys.: Conf. Ser. 1708 012035https://hdl.handle.net/20.500.12585/10035https://iopscience.iop.org/article/10.1088/1742-6596/1708/1/01203510.1088/1742-6596/1708/1/012035Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarDuring the stages of transformation of energy in a process, exercise control over the variables that intervene in it, improve its performance, and identify undesirable conditions in these. Thus, this study is developed as a graphical interface to implement a methodology for controlling variables of energy conversion processes, such as internal combustion engines. The control tool developed in MATLAB variables is based on multivariate statistics. The methods for developing this tool of Graphic User Interface is based on the statistics of principal component analysis and failure statistics such as T2 Hotelling and the Q statistic that allows the control of anomalies presented in the operation's behavior. About the methodology, first, the input data are normalized, achieving standardization of the observation matrix vs. variables, then the spectral decomposition of the normalized data is performed, reaching the generation of the matrix of auto-values, allowing the age of the projection space of the data. With this based and delimited, it is possible to establish the ranges of observation of the mentioned statisticians. The result obtained from this research corresponds to software that allows the constant observation and analysis of the behavior of each variable of the generation engine. It describes the upper limit, lower limit, arithmetic mean, principal components, graphics of the statistics, and detects the failures in real times.8 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_abf2Journal of Physics: Conference Series, Volume 1708Variable control tool in MATLAB for energy transformation processesinfo:eu-repo/semantics/lectureinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_8544http://purl.org/coar/version/c_970fb48d4fbd8a85Data handlingEnergy conversionEnginesMATLABMatrix algebraMultivariant analysisStatisticsLEMBCartagena de IndiasPúblico generalBelussi L, Danza L, Salamone F, Meroni I, Galli S y Svaldi S 2017 Sistema inteligente integrado para una auditoría energética: Metodología y aplicación Energy Procedia 11 231-239Inoue M y Urata K 2017 Evaluación de desempeño y refuerzo en sistema pasivo interconectado IFAC-Papers On Line 50 9999-10004Marofi R 2014 Selección de estrategias de mantenimiento basadas en técnicas AHP y TOPSIS Nat. Sci. 12 163-168Perera L, M Machado, Manguinho D y Valland A 2016 Las fallas del sistema de motores de turbina de gas costa afuera en vista del mantenimiento de la IFAC-paperson Línea 49 280-285Xie C, Grechanik Q y Fu M 2013 IEEE International Conference on Software Maintenance (Edmonton: IEEE) Un diferenciador de GUIBouraoui A, Gharbi I 2019 Model-driven engineering of accessible and multi-platform graphical user interfaces by parameterized model transformations Sci. Comput. Program. 172(63) 101-113Phannachitta P 2020 On an optimal analogy-based software effort estimation Inf. Softw. Technol. 125(10) 106-116Morag I, Chemweno P, Pintelon L, Sheikhalishahi M 2018 Identifying the causes of human error in maintenance work in developing countries. J. Ind. Ergon. 68(12) 222–230Jang I, Kim A, Jung W, Seong P H 2014 An empirical study on the human error recovery failure probability when using soft controls in NPP advanced MCRs Ann. Nucl. Energy 73(12) 373–381Marais H, Van G, Uren K 2019 The merits of exergy-based fault detection in petrochemical processes J. Process Control 74(1) 110–119Zheng T, Tan R, Li Y, Yang B, Shi L, Zhou T 2016 Fault diagnosis of internal combustion engine valve clearance: The survey of the-state-of-the-art Proc. World Congr. Intell. Control Autom. 13(12) 2614–2619Vasu J,. Deb A K, Mukhopadhyay S 2015 MVEM-based fault diagnosis of automotive engines using Dempster-Shafer theory and multiple hypotheses testing IEEE Trans. Syst. Man, Cybern. Syst. 45(7) 977– 989Campos J, Cardenas Y, Valencia G 2017 Trends in Failure Studies of Generation Engines based on Statistical Models from 2007 to 2017 JESTER 11(3) 163–167K Lu, Jin Y, Chen Y, Yang Y, Hou L, Zhang Z, Li Z, Fu C 2019 Review for order reduction based on proper orthogonal decomposition and outlooks of applications in Mech's mechanical systems. Syst. Signal Process. 123(12) 264–297Chiang L, Russell E, Braatz R 2010 Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis Chemometrics and Intelligent Laboratory Systems 12 243–252Boutellaa E, Kerdjidj O, Ghanem K 2019 Covariance matrix based fall detection from multiple wearable sensors J. Biomed. Inform 94(12) 103-119Cardenas Y 2019 Fallas en Bujias para Motores de Generación a Gas (Colombia: Universidad del Atlantico)http://purl.org/coar/resource_type/c_c94fORIGINAL183.pdf183.pdfPonenciaapplication/pdf1231852https://repositorio.utb.edu.co/bitstream/20.500.12585/10035/1/183.pdf2d00cca796346ddaf3409c14cbe3eb1dMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.utb.edu.co/bitstream/20.500.12585/10035/2/license_rdf4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83182https://repositorio.utb.edu.co/bitstream/20.500.12585/10035/3/license.txte20ad307a1c5f3f25af9304a7a7c86b6MD53TEXT183.pdf.txt183.pdf.txtExtracted texttext/plain19558https://repositorio.utb.edu.co/bitstream/20.500.12585/10035/4/183.pdf.txta2ccd76a410784b0594d693c168d9d76MD54THUMBNAIL183.pdf.jpg183.pdf.jpgGenerated 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