Application of neural and bayesian networks in diesel engines under the flaw detection method
The identification of premature faults in Internal Combustion Engines has become determinant to guarantee suitable operation. Therefore, this study focuses on the implementation of fault diagnostic methodology by using advanced algorithms such as Back Propagation neural networks and Bayesian network...
- Autores:
-
Prada Botia, Gaudy Carolina
PABON LEON, JHON ANTUNY
Orjuela Abril, Martha Sofia
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2021
- Institución:
- Universidad Francisco de Paula Santander
- Repositorio:
- Repositorio Digital UFPS
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.ufps.edu.co:ufps/6620
- Acceso en línea:
- https://repositorio.ufps.edu.co/handle/ufps/6620
- Palabra clave:
- Rights
- openAccess
- License
- Published under licence by IOP Publishing Ltd
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Application of neural and bayesian networks in diesel engines under the flaw detection method |
| title |
Application of neural and bayesian networks in diesel engines under the flaw detection method |
| spellingShingle |
Application of neural and bayesian networks in diesel engines under the flaw detection method |
| title_short |
Application of neural and bayesian networks in diesel engines under the flaw detection method |
| title_full |
Application of neural and bayesian networks in diesel engines under the flaw detection method |
| title_fullStr |
Application of neural and bayesian networks in diesel engines under the flaw detection method |
| title_full_unstemmed |
Application of neural and bayesian networks in diesel engines under the flaw detection method |
| title_sort |
Application of neural and bayesian networks in diesel engines under the flaw detection method |
| dc.creator.fl_str_mv |
Prada Botia, Gaudy Carolina PABON LEON, JHON ANTUNY Orjuela Abril, Martha Sofia |
| dc.contributor.author.none.fl_str_mv |
Prada Botia, Gaudy Carolina PABON LEON, JHON ANTUNY Orjuela Abril, Martha Sofia |
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Journal of Physics: Conference Series |
| description |
The identification of premature faults in Internal Combustion Engines has become determinant to guarantee suitable operation. Therefore, this study focuses on the implementation of fault diagnostic methodology by using advanced algorithms such as Back Propagation neural networks and Bayesian networks. Results indicated that the proposed methodology serves as a robust tool to identify different fault conditions in a wide operational spectrum with an reliability of nearly 73%. Moreover, the Backpropagation network diagnostic methodology presented an reliability of 18%, which is 3% higher than Bayesian networks. Overall, the implemented methodology counterbalanced interference conditions and noise signals while providing versatility to operate for different types of engines. In conclusion, this study can be extrapolated to different fields of physics to assist in identifying flaws in experimental test benches. |
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2021 |
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2021-05-22 |
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2022-11-28T01:35:18Z |
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2022-11-28T01:35:18Z |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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https://repositorio.ufps.edu.co/handle/ufps/6620 |
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10.1088/1742-6596/1981/1/012003 |
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10.1088/1742-6596/1981/1/012003 |
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eng |
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Journal of Physics: Conference Series. Vol. 1981 No.012003 (2021) |
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Vol. 1981 N0.012003 (2021) |
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7 |
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012003 (2021) |
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1 |
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Vol.1981 |
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G C Prada Botia et al 2021 J. Phys.: Conf. Ser. 1981 012003 |
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Journal of Physics: Conference Series |
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Published under licence by IOP Publishing Ltd |
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Atribución 4.0 Internacional (CC BY 4.0) |
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Published under licence by IOP Publishing Ltd https://creativecommons.org/licenses/by/4.0/ Atribución 4.0 Internacional (CC BY 4.0) http://purl.org/coar/access_right/c_abf2 |
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08 Páginas |
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Prada Botia, Gaudy Carolinac523b5201cb053007988d9c7731d7cc7600PABON LEON, JHON ANTUNY2adb8fd3199076e6826e922ebbf380bd600Orjuela Abril, Martha Sofia c7eb007995ff675aa1fb2ed07939a53e600Journal of Physics: Conference Series2022-11-28T01:35:18Z2022-11-28T01:35:18Z2021-05-22https://repositorio.ufps.edu.co/handle/ufps/662010.1088/1742-6596/1981/1/012003The identification of premature faults in Internal Combustion Engines has become determinant to guarantee suitable operation. Therefore, this study focuses on the implementation of fault diagnostic methodology by using advanced algorithms such as Back Propagation neural networks and Bayesian networks. Results indicated that the proposed methodology serves as a robust tool to identify different fault conditions in a wide operational spectrum with an reliability of nearly 73%. Moreover, the Backpropagation network diagnostic methodology presented an reliability of 18%, which is 3% higher than Bayesian networks. Overall, the implemented methodology counterbalanced interference conditions and noise signals while providing versatility to operate for different types of engines. In conclusion, this study can be extrapolated to different fields of physics to assist in identifying flaws in experimental test benches.08 Páginasapplication/pdfengJournal of Physics: Conference Series. Vol. 1981 No.012003 (2021)Vol. 1981 N0.012003 (2021)7012003 (2021)1Vol.1981G C Prada Botia et al 2021 J. Phys.: Conf. Ser. 1981 012003Journal of Physics: Conference SeriesPublished under licence by IOP Publishing Ltdhttps://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessAtribución 4.0 Internacional (CC BY 4.0)http://purl.org/coar/access_right/c_abf2https://iopscience.iop.org/article/10.1088/1742-6596/1981/1/012003Application of neural and bayesian networks in diesel engines under the flaw detection methodArtículo 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/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Reino UnidoHernández-Comas B, Maestre-Cambronel D, Pardo-García C, Fonseca-Vigoya M D S, Pabón-León J 2021 Influence of compression rings on the dynamic characteristics and sealing capacity of the combustion chamber in diesel engines Lubricants 9(3) 25Wen L, Li X, Gao L, Zhang Y 2017 A new convolutional neural network-based data-driven fault diagnosis method IEEE Transactions on Industrial Electronics 65(7) 5990Forero J D, Ochoa G V, Alvarado W P 2020 Study of the piston secondary movement on the tribological performance of a single cylinder low-displacement diesel engine Lubricants 8(11) 97Forero J D, Ochoa G V, Alvarado W P 2020 Study of the piston secondary movement on the tribological performance of a single cylinder low-displacement diesel engine Lubricants 8(11) 97Escobar-Yonoff R, Maestre-Cambronel D, Charry S, Rincón-Montenegro A, Portnoy I 2021 Performance assessment and economic perspectives of integrated PEM fuel cell and PEM electrolyzer for electric power generation Heliyon 7(3) e06506Xu X, Yan X, Sheng C, Yuan C, Xu D, Yang J 2017 A belief rule-based expert system for fault diagnosis of marine diesel engines IEEE Trans. Syst. Man, Cybern. Syst. 50(2) 656Zhong J-H, Wong P K, Yang Z-X 2018 Fault diagnosis of rotating machinery based on multiple probabilistic classifiers Mech. Syst. Signal Process. 108(1) 99Bi X, Cao S, Zhang D 2019 Diesel engine valve clearance fault diagnosis based on improved variational mode decomposition and bispectrum Energies 12(4) 661Wei Y, Liu H, Chen G, Ye J 2020 Fault diagnosis of marine turbocharger system based on an unsupervised algorithm J. Electr. Eng. \& Technol. 15(1) 1331-1343Xu X, Zhao Z, Xu X, Yang J, Chang L, Yan X, Wang G 2020 Machine learning-based wear fault diagnosis for marine diesel engine by fusing multiple data-driven models Knowledge-Based Syst. 190 105324Lazakis I, Gkerekos C, Theotokatos G 2019 Investigating an SVM-driven, one-class approach to estimating ship systems condition Ships Offshore Struct. 14(5) 432Liu S, Lü M 2019 Fault diagnosis of the blocking diesel particulate filter based on spectral analysis Processes 7(12) 943Tao J, Qin C, Li W, Liu C 2019 Intelligent fault diagnosis of diesel engines via extreme gradient boosting and high-reliability time--frequency information of vibration signals Sensors 19(15) 3280Valencia Ochoa G, Isaza-Roldan C, Duarte Forero J 2020 Economic and exergo-advance analysis of a waste heat recovery system based on regenerative organic rankine cycle under organic fluids with low global warming potential Energies 13(6) 1317Valencia Ochoa G, Cárdenas Gutierrez J, Duarte Forero J 2020 Exergy, economic, and life-cycle assessment of orc system for waste heat recovery in a natural gas internal combustion engine Resources 9(1) 2Duarte J, Garcia J, Jiménez J, Sanjuan M, Bula A, González J 2017 Auto-ignition control in spark-ignition engines using internal model control structure Journal of Energy Resources Technology 139(2) 022201Alibaba M, Pourdarbani R, Manesh M H K, Ochoa G V, Forero J D 2020 Thermodynamic, exergoeconomic and exergo-environmental analysis of hybrid geothermal-solar power plant based on ORC cycle using emergy concept Heliyon 6(4) e03758Herrera M, Pacheco E C, Forero J D, Lascano A F, Vasquez R 2018 Análisis exergético de un ciclo Brayton supercrítico con dióxido de carbono como fluido de trabajo Exergetic analysis of a supercritical Brayton cycle with carbon dioxide as working fluid Ingecuc 14(1) 159Obregon L, Valencia G, Duarte Forero J 2019 Efficiency optimization study of a centrifugal pump for industrial dredging applications using CFD International Review on Modelling and Simulations (IREMOS) 12(4) 245Orozco T, Herrera M, Duarte Forero J 2019 CFD study of heat exchangers applied in brayton cycles: a case study in supercritical condition using carbon dioxide as working fluid International Review on Modelling and Simulations (IREMOS) 12(2) 72Sanchez De la Hoz J, Valencia G, Duarte Forero J 2019 Reynolds averaged navier–stokes simulations of the airflow in a centrifugal fan using OpenFOAM International Review on Modelling and Simulations (IREMOS) 12(4) 230Consuegra F, Bula A, Guillín W, Sánchez J, Duarte Forero J 2019 Instantaneous in-cylinder volume considering deformation and clearance due to lubricating film in reciprocating internal combustion engines Energies 12(8) 1437ORIGINALApplication of neural and bayesian networks in diesel engines under the flaw detection method.pdfApplication of neural and bayesian networks in diesel engines under the flaw detection 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 incorporada en las Obras Colectivas.

b.	Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.

c.	Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.
Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).

4. Restricciones.
La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:

a.	Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).

b.	Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.

c.	Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.

d.	Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:

i.	Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.

ii.	Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.

e.	Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.

5. Representaciones, Garantías y Limitaciones de Responsabilidad.
A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

6. Limitación de responsabilidad.
A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

7. Término.

a.	Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.

b.	Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.

8. Varios.

a.	Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.

b.	Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.

c.	Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.

d.	Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.
0000-0001-8209-6226c523b5201cb053007988d9c7731d7cc76000000-0002-1078-28572adb8fd3199076e6826e922ebbf380bd6000000-0002-9742-8673c7eb007995ff675aa1fb2ed07939a53e600 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