Identificación de diferentes modos de operación en bombas hidráulicas de caudal fijo con pistones axiales mediante técnicas de aprendizaje automático
ilustraciones, diagramas
- Autores:
-
Alzate Rincón, César Augusto
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/81501
- Palabra clave:
- 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación
Bombas (Máquinas)
Máquinas hidráulicas - mantenimiento
Hydraulic machinery
Pumping machinery
Mantenimiento predictivo
bomba hidráulica de pistones
junta magnética
vibraciones mecánicas
campos magnéticos
emisiones acústicas
aprendizaje automático
predictive maintenance
hydraulic piston pump
magnetic joint
mechanical vibrations
magnetic fields
acoustic emissions
machine learning
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Identificación de diferentes modos de operación en bombas hidráulicas de caudal fijo con pistones axiales mediante técnicas de aprendizaje automático |
| dc.title.translated.eng.fl_str_mv |
Identification of different conditions in fixed displacement hydraulic pumps with axial pistons through machine learning |
| title |
Identificación de diferentes modos de operación en bombas hidráulicas de caudal fijo con pistones axiales mediante técnicas de aprendizaje automático |
| spellingShingle |
Identificación de diferentes modos de operación en bombas hidráulicas de caudal fijo con pistones axiales mediante técnicas de aprendizaje automático 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación Bombas (Máquinas) Máquinas hidráulicas - mantenimiento Hydraulic machinery Pumping machinery Mantenimiento predictivo bomba hidráulica de pistones junta magnética vibraciones mecánicas campos magnéticos emisiones acústicas aprendizaje automático predictive maintenance hydraulic piston pump magnetic joint mechanical vibrations magnetic fields acoustic emissions machine learning |
| title_short |
Identificación de diferentes modos de operación en bombas hidráulicas de caudal fijo con pistones axiales mediante técnicas de aprendizaje automático |
| title_full |
Identificación de diferentes modos de operación en bombas hidráulicas de caudal fijo con pistones axiales mediante técnicas de aprendizaje automático |
| title_fullStr |
Identificación de diferentes modos de operación en bombas hidráulicas de caudal fijo con pistones axiales mediante técnicas de aprendizaje automático |
| title_full_unstemmed |
Identificación de diferentes modos de operación en bombas hidráulicas de caudal fijo con pistones axiales mediante técnicas de aprendizaje automático |
| title_sort |
Identificación de diferentes modos de operación en bombas hidráulicas de caudal fijo con pistones axiales mediante técnicas de aprendizaje automático |
| dc.creator.fl_str_mv |
Alzate Rincón, César Augusto |
| dc.contributor.advisor.none.fl_str_mv |
Restrepo Martínez, Alejandro |
| dc.contributor.author.none.fl_str_mv |
Alzate Rincón, César Augusto |
| dc.subject.ddc.spa.fl_str_mv |
620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación |
| topic |
620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación Bombas (Máquinas) Máquinas hidráulicas - mantenimiento Hydraulic machinery Pumping machinery Mantenimiento predictivo bomba hidráulica de pistones junta magnética vibraciones mecánicas campos magnéticos emisiones acústicas aprendizaje automático predictive maintenance hydraulic piston pump magnetic joint mechanical vibrations magnetic fields acoustic emissions machine learning |
| dc.subject.lemb.none.fl_str_mv |
Bombas (Máquinas) Máquinas hidráulicas - mantenimiento Hydraulic machinery Pumping machinery |
| dc.subject.proposal.spa.fl_str_mv |
Mantenimiento predictivo bomba hidráulica de pistones junta magnética vibraciones mecánicas campos magnéticos emisiones acústicas aprendizaje automático |
| dc.subject.proposal.eng.fl_str_mv |
predictive maintenance hydraulic piston pump magnetic joint mechanical vibrations magnetic fields acoustic emissions machine learning |
| description |
ilustraciones, diagramas |
| publishDate |
2022 |
| dc.date.accessioned.none.fl_str_mv |
2022-06-03T14:59:58Z |
| dc.date.available.none.fl_str_mv |
2022-06-03T14:59:58Z |
| dc.date.issued.none.fl_str_mv |
2022-05 |
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Trabajo de grado - Maestría |
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info:eu-repo/semantics/masterThesis |
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info:eu-repo/semantics/acceptedVersion |
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Text |
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http://purl.org/redcol/resource_type/TM |
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acceptedVersion |
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https://repositorio.unal.edu.co/handle/unal/81501 |
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Universidad Nacional de Colombia |
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Repositorio Institucional Universidad Nacional de Colombia |
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https://repositorio.unal.edu.co/ |
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https://repositorio.unal.edu.co/handle/unal/81501 https://repositorio.unal.edu.co/ |
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Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
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spa |
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Ajit Kumar Pandey, K. Dasgupta, N. Kumar and Alok Vardhan: “Leakage Analysis of Bent-axis Hydro-motors: An Experimental Study”. Journal of the Chinese Society of Mechanical Engineers, Vol.38, No.1, pp 93~98 (2017). Pavel SRB, Michael Petru: “Numérical simulation of composite car seat cushion”. MM Science Journal, june 2020, DOI : 10.17973/MMSJ.2020_06_2020011 Carlo Fiorentini, Caronno Pertusella, Maurizio Corti,Caronno Pertusella: “Method and apparatus for feeding a polyurethane mixture into hollow bodies”. European patent office, EP2366525A1. Pub. No. US 2011/0221085A1 Wanner International Ltd: “Diaphragm pumps for polyurethane production”. World Pumps: Volume 2013, Issue 1, Page 8. January 2013. RAMPF Group, Inc: “Dosing pumps for sealing compounds”. World Pumps: Volume 2009, Issue 519, Pages 20-21. December 2009 Kazushige Nakagawa: “Bent axis type axial piston pump or motor”. European Patent Office, EP0158084B1. Patent Patent Number:4872394. Date of Patent: Oct. 10, 1989 G.S. Highfill, L.A. Halverson: “Lowering total cost of ownership with breakthrough magnetic torque transfer technology”. IEEE Xplore. DOI: 10.1109/CITCON.2006.1635720. Cement Industry Technical Conference, 2006. Conference Record. May 2006 Jie Tao, Yilun Liu, and Dalian Yang: “Bearing Fault Diagnosis Based on Deep Belief Network and Multisensor Information Fusion”. Hindawi Publishing Corporation, Shock and Vibration. Volume 2016, Article ID 9306205, 9 pages August 2016 Christian Lessmeier, James Kuria Kimotho, Detmar Zimmer, Walter Sextro: “Condition Monitoring of Bearing Damage in Electromechanical Drive Systems by Using Motor Current Signals of Electric Motors: A Benchmark Data Set for Data-Driven Classification“. Conference: European Conference of the Prognostics and Health Management Society At: Bilbao, Spain. July 2016 Hui Huang, Ganyong Wu, Yongyuan Wu, Shumei Chen: “Identification of acoustic sources for bent-axis axial pistón motor under variable loads”. Journal of Sound and Vibration 468 (2020) 115063. November 2019 Niloofar Gharesi, Mohammad Mehdi Arefi: “ Analyzing the Vibration Signals for Bearing Defects Diagnosis Using the Combination of SGWT Feature Extraction and SVM “. IFAC PapersOnLine 51-24 (2018) 221–227 Haedong Jeong, Seungtae Park, Sunhee Woo, and Seungchul Lee: “Rotating Machinery Diagnostics using Deep Learning on Orbit Plot Images”. Procedia Manufacturing. Volume 5, 2016, Pages 1107–1118 Miguel Alberto Cabañas: “Monitorización y clasificación de defectos en rodamientos de bolas”. Proyecto fin de carrera, Departamento de ingeniería mecánica, Universidad Carlos III de Madrid. 2011 Qinghong Gong, Hui Hunag, Ganyong Wu, Shumei Chen and Heng Du: ”Analysis and application of a new noise test system for the hydraulic motor”. Journal of Advanced Mechanical Design, Systems, and Manufacturing. Vol.13, No.4. November 2019 B. Sreejith, A.K. Verma and A. Srividya: “ Fault diagnosis of rolling element bearing using time-domain features and neural networks”. IEEE Region 10 Colloquium and the Third ICIIS, Kharagpur, INDIA December 8-10. DOI: 409. 2008 Djamal Zarour, Salim Meziani, Marc Thomas: “ Experimental studies for bearings degradation monitoring at an early stage usyng analysis of variance” Diagnostyka.. DOI: 10.29354/diag/4, Volume 19, issue 4, pages 81-87. 2008 Mansour A. Karkouba, Osama E. Gada, Mahmoud G. Rabie: “ Predicting axial piston pump performance using neural Networks “. Mechanism and Machine Theory, Volume 34, Issue 8, Pages 1211-1226. November 1999 Omar José Lara Castro: “ Nuevas Metodologías no Invasivas de Diagnosis de Defectos Incipientes en Rodamientos de Bola ”. Tesis doctoral. Departamento de ingeniería mecánica, Universidad Carlos III de Madrid, 2007 Paolo Casoli, Mirko Pastori, Fabio Scolari and Massimo Rundo. “A Vibration Signal-Based Method for Fault Identification and Classification in Hydraulic Axial Piston Pumps”. Energies: 12, 953; DOI:10.3390/en12050953. March 2019 ] Mohammadreza Kaji, Jamshid Parvizian, Hans Wernher van de Venn: “Constructing a Reliable Health Indicator for Bearings Using Convolutional Autoencoder and Continuous Wavelet Transform “. Applied Sciences (2076-3417). Vol. 10, Issue 24, p8948. 1p. December 2020 Paresh Girdhar: “Practical machinery vibration analysis and predictive maintenance I title “, eBook ISBN: 9780080480220. Page 6-10. 2004 R Keith Mobley: “Vibration fundamentals, I title II series”, page 3-64. Butterworth Heinemann. 1999 Vimal Saxena, Nilendu Kar Chowdhury, S. Devendiran: “Assessment of Gearbox Fault Detection Using Vibration Signal Analysis and Acoustic Emission Technique “, IOSR Journal of Mechanical and Civil Engineering. (IOSR-JMCE). Volume 7, Issue 4, PP 52-60. August 2013 Frooz Purarjomandlangrudi, Ghavameddin Nourbakhsh: “Acoustic emission condition monitoring: an application for wind turbine fault detection “. International Journal of Research in Engineering and Technology. Volume: 2 Issue: 5 907 – 918. May 2013 Yi-Chang Wu⇑, Bo-Syuan Jian: “Magnetic field analysis of a coaxial magnetic gear mechanism by two-dimensional equivalent magnetic circuit network method and finite-element method “ Applied Mathematical Modelling, Volume 39, Issue 19, Pages 5746-5758. October 2015 G. C. Neves, A. F. F. Filho: “Magnetic Gearing Electromagnetic Concepts “. Journal of Microwaves, Optoelectronics and Electromagnetic Applications. DOI: 10.1590/2179-10742017v16i1874. Vol 16, No 1, March 2017 Fausto Pedro García Márquez, Mayorkinos Papaelias: “ An overview of wind turbine maintenance management “. Non-Destructive Testing and Condition Monitoring Techniques for Renewable Energy Industrial Assets. Chapter 3, Pages 31-47. 2020 Brian E.D. Kingsbury, Nelson Morgan, Steven Greenberg : “Robust speech recognition using the modulation spectrogram”. Speech Communication Volume 25, Issues 1–3, Pages 117-132. August 1998 Achraf Lahrache, Marco Cocconcelli, Riccardo Rubini: “ Anomaly detection in a cutting tool by k-means clustering and support vector machines “. Diagnostyka Vol18, Issue 3, Pag:21–29. March 2017 Mengyu Chai, Jin Zhang, Zaoxiao Zhang, Quan Duan: “Acoustic emission studies for characterization of fatigue crack growth in316LN stainless steel and welds.“. Applied Acoustics, Volume 126, Pages 101-113, November 2017 Changsheng Zhua, Christian Uwa Idemudiaa, Wenfang Fengba: ”Improved logistic regression model for diabetes prediction by integrating PCA and K-means techniques”. Informatics in Medicine Unlocked. DOI: 100179. Volume 17. April 2019 Binu Melit Devassy, Sony George: “Dimensionality reduction and visualisation of hyperspectral ink data using t-SNE”. Forensic Science International. DOI 110194. Volume 311, June 2020 Achraf lahrache, Marco cocconcelli, Riccardo rubini: “Anormaly detection in a cutting tool by k-means clustering and support vector machines”. Diagnostyka, vol 18, Issue 3, pag:21-29, December 2016 Mengyu Chai, Jin Zhang, Zaoxiao Zhang, Quan Duan: “Acoustic emission studies for characterization of fatigue crack growth in316LN stainless steel and welds.“. Applied Acoustics, Volume 126, Pages 101-113, November 2017 Benjamin Lindemann, Fabian Fesenmayr , Nasser Jazdi , Michael Weyrich: “Anormaly detection in discrete manufacturing using self-learning approaches “ Procedia CIRP. Volume 79, Pages 313-318. 2019 Changsheng Zhua, Christian Uwa Idemudiaa, Wenfang Fengba:”Improved logistic regression model for diabetes prediction by integrating PCA and K-means techniques”. Informatics in Medicine Unlocked. DOI: 100179. Volume 17. April 2019 Lucio Mwinmaarong Dery, Benjamin Nachman, Francesco Rubboc, Ariel Schwartzmanc: “ Weakly supervised classication in high energy physics “. Journal of Physics: Conference Series, Volume 1085, Issue 4. July 2017 Donald F. Specht: “ A General Regression Neural Network “. IEEE transtactions of neural networks. DOI: 10.1109/72.97934. Vol 2, No 6. Usa, 1991 Yan-yan, Ying Lu: “Decision tree methods: applications for classification and prediction”. Shanghai Arch Psychiatry DOI: 10.11919/j.issn.1002-0829.215044, Vol 27(2), Pag 130–135. Apr 2015 Johan A. Westerhuis, Huub C. J. Hoefsloot, Suzanne Smit, Daniel J. Vis: “ Assessment of PLSDA cross validation “.Metabolomics Vol 4, Issue 1, pag:81-89. February 2008 A.K. Santra, C. Josephine Christy: “Genetic Algorithm and Confusion Matrix for Document Clustering”. International Journal of Computer Science Issues, Vol. 9, Issue 1, No 2, January 2012 Zhe Hui Hoo, Jane Candlish, Dawn Teare: “ What is an ROC curve? “ Emerg Med J, Vol 34, page:357–359, March 2017 |
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Universidad nacional de Colombia |
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Medellín - Minas - Maestría en Ingeniería Mecánica |
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Facultad de Minas |
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Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Restrepo Martínez, Alejandro85baf6fd49c3422762a87334de5fbbc2Alzate Rincón, César Augusto45824a597f288750c7431dcdf93cd0f62022-06-03T14:59:58Z2022-06-03T14:59:58Z2022-05https://repositorio.unal.edu.co/handle/unal/81501Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramasEste trabajo busca identificar diferentes condiciones de operación en las bombas de pistones axiales de eje desviado mediante técnicas de análisis predictivo; la tesis expone la identificación de diversas situaciones de operación del equipo, que permitan extraer sus principales características de operación y establecer criterios claros de decisión ante posibles fallos en el equipo. La bomba de pistones axiales de eje desviado objeto de este estudio, hace parte de una máquina inyectora de una importante compañía de inyección de poliuretano ubicada en la ciudad de Medellín, donde las mediciones fueron tomadas durante el proceso productivo de inyección de espumas y las diferentes referencias de productos representan sus distintos modos de operación. La toma de datos de operación se dio a través de la medición de señales temporales como vibraciones, emisiones acústicas, ultrasónicas y campos magnéticos, discriminando diferentes niveles de exigencia del equipo. Esta información medida tendrá una etapa de análisis y descripción en el dominio del tiempo y la frecuencia mediante diferentes técnicas, así se facilitará un proceso de clasificación con estrategias de agrupamiento y redes neuronales; y finalmente se examinará el rendimiento de la clasificación con herramientas gráficas de evaluación. El resultado del desarrollo de la tesis hará posible comparar los diferentes métodos utilizados determinando la mejor sugerencia para un plan de diagnóstico de las bombas de pistones, que será el principal insumo de un programa de mantenimiento basado en la condición del equipo que garantice un prolongado ciclo de vida con eficiencia y eficacia en su operación. (Texto tomado de la fuente)This work searches identify different operation conditions of bent axis piston pumps through predictive analysis techniques; the thesis shows various situations of equipment operation, it enables to take its main operating features and establish decision criteria before of equipment fail. The bent axis piston pumps factual of this investigation is part of an injection machine of a major company of polyurethane injection located at Medellin city, where the measures were taken during productive process of foams injection and the different products references represent its modes of operation. The operation data acquisition was given through the measurement of variables like vibrations, acoustic emissions, ultrasonic emissions and magnetic fields, discriminating the exigence levels of equipment. This information will have an analysis and description phase in time and frequency domain through different techniques; so, it will facilitate a classification process with clustering strategies and neural networks; finally, it will review the classification performance whit evaluation graphic tools. The development result will make possible to compare the used methods determining the better suggestion for a piston pumps diagnostic plan, it will be the main input of a maintenance plan based on equipment condition for warranting a long life cycle with efficiency and efficacy in its operation.MaestríaMagíster en Ingeniería MecánicaÁrea Curricular de Ingeniería Mecánicaxiv, 83 páginasapplication/pdfspaUniversidad nacional de ColombiaMedellín - Minas - Maestría en Ingeniería MecánicaDepartamento de Ingeniería MecánicaFacultad de MinasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computaciónBombas (Máquinas)Máquinas hidráulicas - mantenimientoHydraulic machineryPumping machineryMantenimiento predictivobomba hidráulica de pistonesjunta magnéticavibraciones mecánicascampos magnéticosemisiones acústicasaprendizaje automáticopredictive maintenancehydraulic piston pumpmagnetic jointmechanical vibrationsmagnetic fieldsacoustic emissionsmachine learningIdentificación de diferentes modos de operación en bombas hidráulicas de caudal fijo con pistones axiales mediante técnicas de aprendizaje automáticoIdentification of different conditions in fixed displacement hydraulic pumps with axial pistons through machine learningTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAjit Kumar Pandey, K. Dasgupta, N. Kumar and Alok Vardhan: “Leakage Analysis of Bent-axis Hydro-motors: An Experimental Study”. Journal of the Chinese Society of Mechanical Engineers, Vol.38, No.1, pp 93~98 (2017).Pavel SRB, Michael Petru: “Numérical simulation of composite car seat cushion”. MM Science Journal, june 2020, DOI : 10.17973/MMSJ.2020_06_2020011Carlo Fiorentini, Caronno Pertusella, Maurizio Corti,Caronno Pertusella: “Method and apparatus for feeding a polyurethane mixture into hollow bodies”. European patent office, EP2366525A1. Pub. No. US 2011/0221085A1Wanner International Ltd: “Diaphragm pumps for polyurethane production”. World Pumps: Volume 2013, Issue 1, Page 8. January 2013.RAMPF Group, Inc: “Dosing pumps for sealing compounds”. World Pumps: Volume 2009, Issue 519, Pages 20-21. December 2009Kazushige Nakagawa: “Bent axis type axial piston pump or motor”. European Patent Office, EP0158084B1. Patent Patent Number:4872394. Date of Patent: Oct. 10, 1989G.S. Highfill, L.A. Halverson: “Lowering total cost of ownership with breakthrough magnetic torque transfer technology”. IEEE Xplore. DOI: 10.1109/CITCON.2006.1635720. Cement Industry Technical Conference, 2006. Conference Record. May 2006Jie Tao, Yilun Liu, and Dalian Yang: “Bearing Fault Diagnosis Based on Deep Belief Network and Multisensor Information Fusion”. Hindawi Publishing Corporation, Shock and Vibration. Volume 2016, Article ID 9306205, 9 pages August 2016Christian Lessmeier, James Kuria Kimotho, Detmar Zimmer, Walter Sextro: “Condition Monitoring of Bearing Damage in Electromechanical Drive Systems by Using Motor Current Signals of Electric Motors: A Benchmark Data Set for Data-Driven Classification“. Conference: European Conference of the Prognostics and Health Management Society At: Bilbao, Spain. July 2016Hui Huang, Ganyong Wu, Yongyuan Wu, Shumei Chen: “Identification of acoustic sources for bent-axis axial pistón motor under variable loads”. Journal of Sound and Vibration 468 (2020) 115063. November 2019Niloofar Gharesi, Mohammad Mehdi Arefi: “ Analyzing the Vibration Signals for Bearing Defects Diagnosis Using the Combination of SGWT Feature Extraction and SVM “. IFAC PapersOnLine 51-24 (2018) 221–227Haedong Jeong, Seungtae Park, Sunhee Woo, and Seungchul Lee: “Rotating Machinery Diagnostics using Deep Learning on Orbit Plot Images”. Procedia Manufacturing. Volume 5, 2016, Pages 1107–1118Miguel Alberto Cabañas: “Monitorización y clasificación de defectos en rodamientos de bolas”. Proyecto fin de carrera, Departamento de ingeniería mecánica, Universidad Carlos III de Madrid. 2011Qinghong Gong, Hui Hunag, Ganyong Wu, Shumei Chen and Heng Du: ”Analysis and application of a new noise test system for the hydraulic motor”. Journal of Advanced Mechanical Design, Systems, and Manufacturing. Vol.13, No.4. November 2019B. Sreejith, A.K. Verma and A. Srividya: “ Fault diagnosis of rolling element bearing using time-domain features and neural networks”. IEEE Region 10 Colloquium and the Third ICIIS, Kharagpur, INDIA December 8-10. DOI: 409. 2008Djamal Zarour, Salim Meziani, Marc Thomas: “ Experimental studies for bearings degradation monitoring at an early stage usyng analysis of variance” Diagnostyka.. DOI: 10.29354/diag/4, Volume 19, issue 4, pages 81-87. 2008Mansour A. Karkouba, Osama E. Gada, Mahmoud G. Rabie: “ Predicting axial piston pump performance using neural Networks “. Mechanism and Machine Theory, Volume 34, Issue 8, Pages 1211-1226. November 1999Omar José Lara Castro: “ Nuevas Metodologías no Invasivas de Diagnosis de Defectos Incipientes en Rodamientos de Bola ”. Tesis doctoral. Departamento de ingeniería mecánica, Universidad Carlos III de Madrid, 2007Paolo Casoli, Mirko Pastori, Fabio Scolari and Massimo Rundo. “A Vibration Signal-Based Method for Fault Identification and Classification in Hydraulic Axial Piston Pumps”. Energies: 12, 953; DOI:10.3390/en12050953. March 2019] Mohammadreza Kaji, Jamshid Parvizian, Hans Wernher van de Venn: “Constructing a Reliable Health Indicator for Bearings Using Convolutional Autoencoder and Continuous Wavelet Transform “. Applied Sciences (2076-3417). Vol. 10, Issue 24, p8948. 1p. December 2020Paresh Girdhar: “Practical machinery vibration analysis and predictive maintenance I title “, eBook ISBN: 9780080480220. Page 6-10. 2004R Keith Mobley: “Vibration fundamentals, I title II series”, page 3-64. Butterworth Heinemann. 1999Vimal Saxena, Nilendu Kar Chowdhury, S. Devendiran: “Assessment of Gearbox Fault Detection Using Vibration Signal Analysis and Acoustic Emission Technique “, IOSR Journal of Mechanical and Civil Engineering. (IOSR-JMCE). Volume 7, Issue 4, PP 52-60. August 2013Frooz Purarjomandlangrudi, Ghavameddin Nourbakhsh: “Acoustic emission condition monitoring: an application for wind turbine fault detection “. International Journal of Research in Engineering and Technology. Volume: 2 Issue: 5 907 – 918. May 2013Yi-Chang Wu⇑, Bo-Syuan Jian: “Magnetic field analysis of a coaxial magnetic gear mechanism by two-dimensional equivalent magnetic circuit network method and finite-element method “ Applied Mathematical Modelling, Volume 39, Issue 19, Pages 5746-5758. October 2015G. C. Neves, A. F. F. Filho: “Magnetic Gearing Electromagnetic Concepts “. Journal of Microwaves, Optoelectronics and Electromagnetic Applications. DOI: 10.1590/2179-10742017v16i1874. 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International Journal of Computer Science Issues, Vol. 9, Issue 1, No 2, January 2012Zhe Hui Hoo, Jane Candlish, Dawn Teare: “ What is an ROC curve? “ Emerg Med J, Vol 34, page:357–359, March 2017EstudiantesInvestigadoresPúblico generalORIGINAL1053783673.2022.pdf1053783673.2022.pdfTesis de maestría en ingeniería mecánicaapplication/pdf2284594https://repositorio.unal.edu.co/bitstream/unal/81501/1/1053783673.2022.pdf9598dfda6f8a9a6ac46927305fd69d10MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/81501/2/license.txt8153f7789df02f0a4c9e079953658ab2MD52THUMBNAIL1053783673.2022.pdf.jpg1053783673.2022.pdf.jpgGenerated Thumbnailimage/jpeg5987https://repositorio.unal.edu.co/bitstream/unal/81501/3/1053783673.2022.pdf.jpg60c398d41f8784d99368fc4a6338a6a3MD53unal/81501oai:repositorio.unal.edu.co:unal/815012024-08-06 23:09:58.405Repositorio Institucional Universidad Nacional de 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