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:
- 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:11323/7911
- Acceso en línea:
- https://hdl.handle.net/11323/7911
https://repositorio.cuc.edu.co/
- Palabra clave:
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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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 |
| 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.spa.fl_str_mv |
Cardenas, Y Carrillo, G E Alviz, A Carrillo, G |
| 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 T! 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. |
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2020 |
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2020 |
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2021-02-23T18:51:56Z |
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2021-02-23T18:51:56Z |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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0.1088/1742-6596/1708/1/012035 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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https://hdl.handle.net/11323/7911 https://repositorio.cuc.edu.co/ |
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eng |
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eng |
| dc.relation.references.spa.fl_str_mv |
[1] Belussi L, Danza L, Salamone F, Meroni I, Galli S and Svaldi S 2017 Integrated smart system for an energy audit: Methodology and application Energy Procedia 11 231-239 [2] Inoue M and Urata K 2017 Performance evaluation and reinforcement in interconnected passive system IFAC-Papers On Line 50 9999-10004 [3] Marofi R 2014 Selection of maintenance strategies based on AHP and TOPSIS techniques Nat. Sci. 12 163-168 [4] Perera L, Machado M, Manguinho D and Valland A 2016 System failures of offshore gas turbine engines in maintenance perspective IFAC-PapersOn Line 49 280-285 [5] Xie C, Grechanik Q and Fu M 2013 IEEE International Conference on Software Maintenance (Edmonton: IEEE) A GUI differentiator [6] Bouraoui A and Gharbi I 2019 Model-driven engineering of accessible and multi-platform graphical user interfaces by parameterized model transformations Sci. Comput. Program. 172 101-113 [7] Phannachitta P 2020 On an optimal analogy-based software effort estimation Inf. Softw. Technol. 125 106-116 [8] Morag I, Chemweno P, Pintelon L and Sheikhalishahi M 2018 Identifying the causes of human error in maintenance work in developing countries J. Ind. Ergon. 68 222-230 [9] Jang I, Kim A, Jung W and 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 73 373-381 [10] Marais H, Van G and Uren K 2019 The merits of exergy-based fault detection in petrochemical processes J. Process Control 74 110-119 [11] Zheng T, Tan R, Li Y, Yang B, Shi L and 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 2614-2619 [12] Vasu J, Deb A K and Mukhopadhyay S 2015 MVEM-based fault diagnosis of automotive engines using Dempster-Shafer theory and multiple hypotheses testing IEEE Trans. Syst. Man, Cybern. Syst. 45 977-989 [13] Campos J, Cardenas Y and Valencia G 2017 Trends in Failure Studies of Generation Engines based on Statistical Models from 2007 to 2017 JESTER 11 163-167 [14] Lu K, Jin Y, Chen Y, Yang Y, Hou L, Zhang Z, Li Z and Fu C 2019 Review for order reduction based on proper orthogonal decomposition and outlooks of applications in Mech’s mechanical systems Syst. Signal Process. 123 264-297 [15] Chiang L, Russell E and 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 12 243-252 [16] Boutellaa E, Kerdjidj O and Ghanem K 2019 Covariance matrix based fall detection from multiple wearable sensors J. Biomed. Inform 94 103-119 [17] Cardenas Y 2019 Fallas en Bujias para Motores de Generatión a Gas (Colombia: Universidad del Atlantico) |
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Attribution-NonCommercial-NoDerivatives 4.0 International |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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Cardenas, YCarrillo, G EAlviz, ACarrillo, G2021-02-23T18:51:56Z2021-02-23T18:51:56Z2020https://hdl.handle.net/11323/79110.1088/1742-6596/1708/1/012035Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/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 T! 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.Cardenas, YCarrillo, G EAlviz, ACarrillo, Gapplication/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Journal of Physics: Conference Serieshttps://iopscience.iop.org/article/10.1088/1742-6596/1708/1/012035Variable control tool in MATLAB for energy transformation processesArtículo 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/acceptedVersion[1] Belussi L, Danza L, Salamone F, Meroni I, Galli S and Svaldi S 2017 Integrated smart system for an energy audit: Methodology and application Energy Procedia 11 231-239[2] Inoue M and Urata K 2017 Performance evaluation and reinforcement in interconnected passive system IFAC-Papers On Line 50 9999-10004[3] Marofi R 2014 Selection of maintenance strategies based on AHP and TOPSIS techniques Nat. Sci. 12 163-168[4] Perera L, Machado M, Manguinho D and Valland A 2016 System failures of offshore gas turbine engines in maintenance perspective IFAC-PapersOn Line 49 280-285[5] Xie C, Grechanik Q and Fu M 2013 IEEE International Conference on Software Maintenance (Edmonton: IEEE) A GUI differentiator[6] Bouraoui A and Gharbi I 2019 Model-driven engineering of accessible and multi-platform graphical user interfaces by parameterized model transformations Sci. Comput. Program. 172 101-113[7] Phannachitta P 2020 On an optimal analogy-based software effort estimation Inf. Softw. Technol. 125 106-116[8] Morag I, Chemweno P, Pintelon L and Sheikhalishahi M 2018 Identifying the causes of human error in maintenance work in developing countries J. Ind. Ergon. 68 222-230[9] Jang I, Kim A, Jung W and 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 73 373-381[10] Marais H, Van G and Uren K 2019 The merits of exergy-based fault detection in petrochemical processes J. Process Control 74 110-119[11] Zheng T, Tan R, Li Y, Yang B, Shi L and 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 2614-2619[12] Vasu J, Deb A K and Mukhopadhyay S 2015 MVEM-based fault diagnosis of automotive engines using Dempster-Shafer theory and multiple hypotheses testing IEEE Trans. Syst. Man, Cybern. Syst. 45 977-989[13] Campos J, Cardenas Y and Valencia G 2017 Trends in Failure Studies of Generation Engines based on Statistical Models from 2007 to 2017 JESTER 11 163-167[14] Lu K, Jin Y, Chen Y, Yang Y, Hou L, Zhang Z, Li Z and Fu C 2019 Review for order reduction based on proper orthogonal decomposition and outlooks of applications in Mech’s mechanical systems Syst. Signal Process. 123 264-297[15] Chiang L, Russell E and 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 12 243-252[16] Boutellaa E, Kerdjidj O and Ghanem K 2019 Covariance matrix based fall detection from multiple wearable sensors J. Biomed. Inform 94 103-119[17] Cardenas Y 2019 Fallas en Bujias para Motores de Generatión a Gas (Colombia: Universidad del Atlantico)PublicationCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.cuc.edu.co/bitstreams/1d8c443b-c679-4c5c-9f41-afc1749202c9/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/0a228231-bc37-434b-a870-f00eb53eaf74/downloade30e9215131d99561d40d6b0abbe9badMD53ORIGINALVariable control tool in MATLAB for energy transformation processes.pdfVariable control tool in MATLAB for energy transformation processes.pdfapplication/pdf93498https://repositorio.cuc.edu.co/bitstreams/f8fa9e7d-96a9-4207-a3c4-bc413e6fc294/download287904e23a6c0da6e89d1a3a9be3f1ebMD51THUMBNAILVariable control tool in MATLAB for energy transformation processes.pdf.jpgVariable control tool in MATLAB for energy transformation 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