Fuzzy logic methodology to study the behavior of energy transformation processes based on statistics t2 and 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:
- 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:11323/7885
- Acceso en línea:
- https://hdl.handle.net/11323/7885
http://doi.org/10.1088/1742-6596/1708/1/012034
https://repositorio.cuc.edu.co/
- Palabra clave:
- MATLAB
fuzzy Mandani type logic
analysis of energy transformation processes
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Fuzzy logic methodology to study the behavior of energy transformation processes based on statistics t2 and q |
| title |
Fuzzy logic methodology to study the behavior of energy transformation processes based on statistics t2 and q |
| spellingShingle |
Fuzzy logic methodology to study the behavior of energy transformation processes based on statistics t2 and q MATLAB fuzzy Mandani type logic analysis of energy transformation processes |
| title_short |
Fuzzy logic methodology to study the behavior of energy transformation processes based on statistics t2 and q |
| title_full |
Fuzzy logic methodology to study the behavior of energy transformation processes based on statistics t2 and q |
| title_fullStr |
Fuzzy logic methodology to study the behavior of energy transformation processes based on statistics t2 and q |
| title_full_unstemmed |
Fuzzy logic methodology to study the behavior of energy transformation processes based on statistics t2 and q |
| title_sort |
Fuzzy logic methodology to study the behavior of energy transformation processes based on statistics t2 and q |
| dc.creator.fl_str_mv |
Cárdenas, Y Carrillo, G E Alviz, A Carrillo, G |
| dc.contributor.author.spa.fl_str_mv |
Cárdenas, Y Carrillo, G E Alviz, A Carrillo, G |
| dc.subject.spa.fl_str_mv |
MATLAB fuzzy Mandani type logic analysis of energy transformation processes |
| topic |
MATLAB fuzzy Mandani type logic analysis of energy transformation processes |
| 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 |
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2021-02-19T16:55:26Z |
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2021-02-19T16:55:26Z |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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https://hdl.handle.net/11323/7885 |
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http://doi.org/10.1088/1742-6596/1708/1/012034 |
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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/11323/7885 http://doi.org/10.1088/1742-6596/1708/1/012034 https://repositorio.cuc.edu.co/ |
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Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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eng |
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eng |
| dc.relation.references.spa.fl_str_mv |
Li Z, Sun L, Geng Y, Dong H, Ren J, Liug Z, Tian X, Yabara H and 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 70-82 Islam J, Hu Y, Haltas I, Balta-ozkan N, Jr G and 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 1153-1178 Franciosi C, Voisin A, Miranda S, Riemma S and Iung B 2020 Measuring maintenance impacts on the sustainability of manufacturing industries: from a systematic literature review to a framework proposal J. Clean. Prod. 260 121-129 Waligórski M, Batura K, Kucal K and Merkisz J 2020 Research on airplanes engines dynamic processes with modern acoustic methods for fast and accurate diagnostics and safety improvement Measurement 12 123-129 Diéguez M, Urroz J, Sáinz D, Machin J, Arana M and Gandía L 2018 Characterization of combustion anomalies in a hydrogen-fueled 1. 4 L commercial spark-ignition engine using in-cylinder pressure, block-engine vibration, and acoustic measurements Energy Convers. Manag. 172 67-80 Alblawi 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 1083-1096 Khelil Y, Graton G, Djeziri M, Ouladsine M and Outbib R 2012 Fault detection and isolation in marine diesel engines-a generic methodology IFAC Proc. 45 964-969 Tayarani S S and Khorasani K Fault detection and isolation of gas turbine engines using a bank of neural networks J. Process Control 36 41-48 Delvecchio S, Bonfiglio P and Pompoli F 2018 Vibro-acoustic condition monitoring of internal combustion engines: A critical review of existing techniques Mech. Syst. Signal Process 99 661-683 Çeven S, Albayrak A and Bayır R 2020 Real-time range estimation in electric vehicles using fuzzy Comput. Electr. Eng. 34 83-89 Ansari F 2020 Cost-based text understanding to improve maintenance knowledge intelligence in manufacturing enterprises Comput. Ind. Eng. 141 106-115 Lin Q, Zhang Y, Yang S, Ma S, Zhang T and Xiao Q 2020 Full length Article A self-learning and self-optimizing framework for the fault diagnosis knowledge base in a workshop Robot. Comput. Integer Manuf. 65 101-121 Tso W, Burnak B and Pistikopoulos E 2020 HY-POP: Hyperparameter optimization of machine learning models through parametric programming Comput. Chem. Eng. 139 106-113 Sangha M, Gomm J, Yu D and Page G 2005 Fault detection and identification of automotive engines using neural networks IFAC Proc. 38 272-277 Zumoffen 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) |
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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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Cárdenas, YCarrillo, G EAlviz, ACarrillo, G2021-02-19T16:55:26Z2021-02-19T16:55:26Z2020https://hdl.handle.net/11323/7885http://doi.org/10.1088/1742-6596/1708/1/012034Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/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.Cárdenas, 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/10.1088/1742-6596/1708/1/012034/metaMATLABfuzzy Mandani type logicanalysis of energy transformation processesFuzzy logic methodology to study the behavior of energy transformation processes based on statistics t2 and qArtículo 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/acceptedVersionLi Z, Sun L, Geng Y, Dong H, Ren J, Liug Z, Tian X, Yabara H and 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 70-82Islam J, Hu Y, Haltas I, Balta-ozkan N, Jr G and 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 1153-1178Franciosi C, Voisin A, Miranda S, Riemma S and Iung B 2020 Measuring maintenance impacts on the sustainability of manufacturing industries: from a systematic literature review to a framework proposal J. Clean. Prod. 260 121-129Waligórski M, Batura K, Kucal K and Merkisz J 2020 Research on airplanes engines dynamic processes with modern acoustic methods for fast and accurate diagnostics and safety improvement Measurement 12 123-129Diéguez M, Urroz J, Sáinz D, Machin J, Arana M and Gandía L 2018 Characterization of combustion anomalies in a hydrogen-fueled 1. 4 L commercial spark-ignition engine using in-cylinder pressure, block-engine vibration, and acoustic measurements Energy Convers. Manag. 172 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 1083-1096Khelil Y, Graton G, Djeziri M, Ouladsine M and Outbib R 2012 Fault detection and isolation in marine diesel engines-a generic methodology IFAC Proc. 45 964-969Tayarani S S and Khorasani K Fault detection and isolation of gas turbine engines using a bank of neural networks J. Process Control 36 41-48Delvecchio S, Bonfiglio P and Pompoli F 2018 Vibro-acoustic condition monitoring of internal combustion engines: A critical review of existing techniques Mech. Syst. Signal Process 99 661-683Çeven S, Albayrak A and Bayır R 2020 Real-time range estimation in electric vehicles using fuzzy Comput. Electr. Eng. 34 83-89Ansari F 2020 Cost-based text understanding to improve maintenance knowledge intelligence in manufacturing enterprises Comput. Ind. Eng. 141 106-115Lin Q, Zhang Y, Yang S, Ma S, Zhang T and Xiao Q 2020 Full length Article A self-learning and self-optimizing framework for the fault diagnosis knowledge base in a workshop Robot. Comput. Integer Manuf. 65 101-121Tso W, Burnak B and Pistikopoulos E 2020 HY-POP: Hyperparameter optimization of machine learning models through parametric programming Comput. Chem. Eng. 139 106-113Sangha M, Gomm J, Yu D and Page G 2005 Fault detection and identification of automotive engines using neural networks IFAC Proc. 38 272-277Zumoffen 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)PublicationLICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/eca485d8-9488-46f6-8f0d-7f23166ae305/downloade30e9215131d99561d40d6b0abbe9badMD53ORIGINALFuzzy logic methodology to study the behavior of energy transformation.pdfFuzzy logic methodology to study the behavior of energy transformation.pdfapplication/pdf1282435https://repositorio.cuc.edu.co/bitstreams/ea787ce0-51c4-4603-a1c7-b48775c52a3b/download57c8159ae1d546149bcc732beb3affddMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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