Fuzzy logic methodology to study the behavior of energy transformation processes based on statistics T2and Q
In the processes of energy transformation, to carry out an adequate follow-up of the process parameters represent an opportunity to propose strategies to improve the processes' performance. For this reason, it is essential to analyze the behavior of process variables under the quantitative and...
- Autores:
-
Cárdenas, 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:20.500.12585/9982
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/9982
https://iopscience.iop.org/article/10.1088/1742-6596/1708/1/012034/meta
- Palabra clave:
- Computer circuits
Decision making
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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dc.title.spa.fl_str_mv |
Fuzzy logic methodology to study the behavior of energy transformation processes based on statistics T2and Q |
title |
Fuzzy logic methodology to study the behavior of energy transformation processes based on statistics T2and Q |
spellingShingle |
Fuzzy logic methodology to study the behavior of energy transformation processes based on statistics T2and Q Computer circuits Decision making LEMB |
title_short |
Fuzzy logic methodology to study the behavior of energy transformation processes based on statistics T2and Q |
title_full |
Fuzzy logic methodology to study the behavior of energy transformation processes based on statistics T2and Q |
title_fullStr |
Fuzzy logic methodology to study the behavior of energy transformation processes based on statistics T2and Q |
title_full_unstemmed |
Fuzzy logic methodology to study the behavior of energy transformation processes based on statistics T2and Q |
title_sort |
Fuzzy logic methodology to study the behavior of energy transformation processes based on statistics T2and Q |
dc.creator.fl_str_mv |
Cárdenas, Y Carrillo, G E Alviz, A Carrillo, G |
dc.contributor.author.none.fl_str_mv |
Cárdenas, Y Carrillo, G E Alviz, A Carrillo, G |
dc.subject.keywords.spa.fl_str_mv |
Computer circuits Decision making |
topic |
Computer circuits Decision making LEMB |
dc.subject.armarc.none.fl_str_mv |
LEMB |
description |
In the processes of energy transformation, to carry out an adequate follow-up of the process parameters represent an opportunity to propose strategies to improve the processes' performance. For this reason, it is essential to analyze the behavior of process variables under the quantitative and qualitative optics supported by the experts. Thus, this work proposes a methodology of fuzzy Mandani type logic that allows the analysis of energy transformation processes (such as internal combustion engines) based on T2 and Q statistics, as a way to identify whether the operation limits are kept within the normal or exceed the limits, achieving to identify the anomaly in the process. In the initial stage, MATLAB implements two diffuse systems; the first system aims to determine the impact variables have on the generation of an anomaly, without identifying the type of defect. In the second stage, it's defined as a function of the number guests, the kind of monster that occurs in the observations made from the transition range in the operation of the system analyzed, until the last measurement obtained. In the third stage, the statistics T2, Q, and its limits are determined from the operating variables of the selected system. Finally, the previously calculated statistics are graphically processed in the diffuse systems. The results obtained in this work show that the analysis of processes or phenomena based on qualitative observations, the methodology implemented, is a useful tool for decision making in the industrial sector. |
publishDate |
2020 |
dc.date.issued.none.fl_str_mv |
2020-10 |
dc.date.accessioned.none.fl_str_mv |
2021-02-10T20:28:59Z |
dc.date.available.none.fl_str_mv |
2021-02-10T20:28:59Z |
dc.date.submitted.none.fl_str_mv |
2021-02-08 |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/lecture |
dc.type.hasVersion.spa.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.spa.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_8544 |
status_str |
publishedVersion |
dc.identifier.citation.spa.fl_str_mv |
Y Cárdenas et al 2020 J. Phys.: Conf. Ser. 1708 012034 |
dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12585/9982 |
dc.identifier.url.none.fl_str_mv |
https://iopscience.iop.org/article/10.1088/1742-6596/1708/1/012034/meta |
dc.identifier.doi.none.fl_str_mv |
10.1088/1742-6596/1708/1/012034 |
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 |
Y Cárdenas et al 2020 J. Phys.: Conf. Ser. 1708 012034 10.1088/1742-6596/1708/1/012034 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
url |
https://hdl.handle.net/20.500.12585/9982 https://iopscience.iop.org/article/10.1088/1742-6596/1708/1/012034/meta |
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 |
8 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 |
Journal of Physics: Conference Series 1708 (2020) 012034 |
institution |
Universidad Tecnológica de Bolívar |
bitstream.url.fl_str_mv |
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Cárdenas, Y70344b45-b17b-43d3-8513-8f568098aea6Carrillo, G Ed1de93af-aa79-478f-9d48-0f302581d99fAlviz, Afb38fec4-6d03-488d-8cac-a3ca08b71552Carrillo, Ga1e8c876-c03d-48dd-a0bd-1fabfc1444932021-02-10T20:28:59Z2021-02-10T20:28:59Z2020-102021-02-08Y Cárdenas et al 2020 J. Phys.: Conf. Ser. 1708 012034https://hdl.handle.net/20.500.12585/9982https://iopscience.iop.org/article/10.1088/1742-6596/1708/1/012034/meta10.1088/1742-6596/1708/1/012034Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarIn the processes of energy transformation, to carry out an adequate follow-up of the process parameters represent an opportunity to propose strategies to improve the processes' performance. For this reason, it is essential to analyze the behavior of process variables under the quantitative and qualitative optics supported by the experts. Thus, this work proposes a methodology of fuzzy Mandani type logic that allows the analysis of energy transformation processes (such as internal combustion engines) based on T2 and Q statistics, as a way to identify whether the operation limits are kept within the normal or exceed the limits, achieving to identify the anomaly in the process. In the initial stage, MATLAB implements two diffuse systems; the first system aims to determine the impact variables have on the generation of an anomaly, without identifying the type of defect. In the second stage, it's defined as a function of the number guests, the kind of monster that occurs in the observations made from the transition range in the operation of the system analyzed, until the last measurement obtained. In the third stage, the statistics T2, Q, and its limits are determined from the operating variables of the selected system. Finally, the previously calculated statistics are graphically processed in the diffuse systems. The results obtained in this work show that the analysis of processes or phenomena based on qualitative observations, the methodology implemented, is a useful tool for decision making in the industrial sector.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 1708 (2020) 012034Fuzzy logic methodology to study the behavior of energy transformation processes based on statistics T2and Qinfo:eu-repo/semantics/lectureinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_8544http://purl.org/coar/version/c_970fb48d4fbd8a85Computer circuitsDecision makingLEMBCartagena de IndiasInvestigadoresLi Z, Sun L, Geng Y, Dong H, Ren J, Liug Z, Tian X, Yabara H, Higanoa Y 2017 Examining industrial structure changes and corresponding carbon emissionreduction effect by combining input-output analysis and social network analysis: A comparison study of China and Japan J. Clean. Prod. 162(61) 70-82Islam J, Hu Y, Haltas I, Balta-ozkan N, G Jr, Varga L 2018 Reducing industrial energy demand in the UK: A review of energy e ffi ciency technologies and energy-saving potentia in selected sectors Renew. Sustain. Energy Rev. 94(23) 1153–1178Franciosi C, Voisin A, Miranda S, Riemma S, Iung B 2020 Measuring maintenance impacts on the sustainability of manufacturing industries: from a systematic literature review to a framework proposal J. Clean. Prod. 260(14) 121-129Waligórski M, Batura K, Kucal K, Merkisz J 2020 Research on airplanes engines dynamic processes with modern acoustic methods for fast and accurate diagnostics and safety improvement Measurement 12(13) 123-129Diéguez M, Urroz J, Sáinz D, Machin J, Arana M, Gandía L 2018 Characterization of combustion anomalies in a hydrogen-fueled 1. 4 L commercial spark-ignition engine using in-cylinder pressure, blockengine vibration, and acoustic measurements Energy Convers. Manag. 172(13) 67–80Alblawi A 2020 Fault diagnosis of an industrial gas turbine based on the thermodynamic model coupled with a multi feedforward artificial neural networks Energy Reports 6(13) 1083–1096Khelil Y, Graton G, Djeziri M, Ouladsine M, R Outbib 2012 Fault detection and isolation in marine diesel engines-a generic methodology IFAC Proc. 45(20) 964–969Tayarani S S, Khorasani K Fault detection and isolation of gas turbine engines using a bank of neural networks J. Process Control 36(22) 41-48Delvecchio S, Bonfiglio P, Pompoli F 2018 Vibro-acoustic condition monitoring of internal combustion engines: A critical review of existing techniques Mech. Syst. Signal Process 99(14) 661–683Çeven S, Albayrak A, Bayır R 2020 Real-time range estimation in electric vehicles using fuzzy Comput. Electr. Eng. 34(13) 83-89Ansari F 2020 Cost-based text understanding to improve maintenance knowledge intelligence in manufacturing enterprises Comput. Ind. Eng. 141(12) 106-115Lin Q, Zhang Y, Yang S, Ma S, Zhang T, Xiao Q 2020 Full length Article A self-learning and selfoptimizing framework for the fault diagnosis knowledge base in a workshop Robot. Comput. Integer Manuf. 65(12) 101-121Tso W, Burnak B, Pistikopoulos E 2020 HY-POP: Hyperparameter optimization of machine learning models through parametric programming Comput. Chem. Eng. 139(13) 106-113Sangha M, Gomm J, Yu D, Page G 2005 Fault detection and identification of automotive engines using neural networks IFAC Proc. 38(12005) 272–277Zumoffen D 2008 Desarrollo de Sistemas de Diagnóstico de Fallas Integrado al Diseño de Control Tolerante a Fallas en Procesos Químicos (Colombia: Universidad Nacional de Rosario)http://purl.org/coar/resource_type/c_c94fCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.utb.edu.co/bitstream/20.500.12585/9982/2/license_rdf4460e5956bc1d1639be9ae6146a50347MD52ORIGINAL140.pdf140.pdfPonenciaapplication/pdf1282436https://repositorio.utb.edu.co/bitstream/20.500.12585/9982/1/140.pdf59fd30535af81fe45f3a398f1373442eMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-83182https://repositorio.utb.edu.co/bitstream/20.500.12585/9982/3/license.txte20ad307a1c5f3f25af9304a7a7c86b6MD53TEXT140.pdf.txt140.pdf.txtExtracted 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