Diseño, implementación y validación de herramienta de diagnóstico temprano de motores eléctricos basado en audio.
Este documento presenta el diseño, implementación y validación de herramienta de diagnostico temprano de motores eléctricos basado en audio para compresores de referencia NC4AV80ALR de la marca SAMSUNG, los cuales se encuentran en neveras. El diseño se basa en un análisis preliminar de la señal acús...
- Autores:
-
Escobar Mafla, Lennin Edmundo
- Tipo de recurso:
- Fecha de publicación:
- 2023
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/84834
- Palabra clave:
- 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación
Electrodomésticos
Electricidad-aparatos e instrumentos
Motores eléctricos
Household appliances, electric
Electric apparatus and appliances
Electric motors
Falla
Diagnóstico
acústico
Fault
Diagnosis
Acoustic
Audio
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional
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Diseño, implementación y validación de herramienta de diagnóstico temprano de motores eléctricos basado en audio. |
| dc.title.translated.eng.fl_str_mv |
Design, implementation, and validation of an early diagnosis tool for electric motors based on audio. |
| title |
Diseño, implementación y validación de herramienta de diagnóstico temprano de motores eléctricos basado en audio. |
| spellingShingle |
Diseño, implementación y validación de herramienta de diagnóstico temprano de motores eléctricos basado en audio. 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación Electrodomésticos Electricidad-aparatos e instrumentos Motores eléctricos Household appliances, electric Electric apparatus and appliances Electric motors Falla Diagnóstico acústico Fault Diagnosis Acoustic Audio |
| title_short |
Diseño, implementación y validación de herramienta de diagnóstico temprano de motores eléctricos basado en audio. |
| title_full |
Diseño, implementación y validación de herramienta de diagnóstico temprano de motores eléctricos basado en audio. |
| title_fullStr |
Diseño, implementación y validación de herramienta de diagnóstico temprano de motores eléctricos basado en audio. |
| title_full_unstemmed |
Diseño, implementación y validación de herramienta de diagnóstico temprano de motores eléctricos basado en audio. |
| title_sort |
Diseño, implementación y validación de herramienta de diagnóstico temprano de motores eléctricos basado en audio. |
| dc.creator.fl_str_mv |
Escobar Mafla, Lennin Edmundo |
| dc.contributor.advisor.none.fl_str_mv |
Camargo Bareño, Carlos Iván |
| dc.contributor.author.none.fl_str_mv |
Escobar Mafla, Lennin Edmundo |
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Computación Científica |
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0000-0002-5676-6019 |
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LEscobar |
| dc.subject.ddc.spa.fl_str_mv |
000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación |
| topic |
000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación Electrodomésticos Electricidad-aparatos e instrumentos Motores eléctricos Household appliances, electric Electric apparatus and appliances Electric motors Falla Diagnóstico acústico Fault Diagnosis Acoustic Audio |
| dc.subject.lemb.spa.fl_str_mv |
Electrodomésticos Electricidad-aparatos e instrumentos Motores eléctricos |
| dc.subject.lemb.eng.fl_str_mv |
Household appliances, electric Electric apparatus and appliances Electric motors |
| dc.subject.proposal.spa.fl_str_mv |
Falla Diagnóstico acústico |
| dc.subject.proposal.eng.fl_str_mv |
Fault Diagnosis Acoustic Audio |
| description |
Este documento presenta el diseño, implementación y validación de herramienta de diagnostico temprano de motores eléctricos basado en audio para compresores de referencia NC4AV80ALR de la marca SAMSUNG, los cuales se encuentran en neveras. El diseño se basa en un análisis preliminar de la señal acústica emitida por el compresor en campo cercano mediante el uso de un micrófono, con el fin de seleccionar el punto en el espacio que presente las mejores características en pro de la calidad en la toma de las muestras, para esto, se analizan características como el valor RMS, frecuencia de roll-off y el centroide espectral. Como siguiente paso se crea un conjunto de datos de audio que consta de 25 compresores distribuidos equitativamente en 5 clases de las cuales 2 clases pertenecen a compresores que operan dentro de sus parámetros normales y las 3 clases restantes provienen de compresores que presentan fallas en su funcionamiento. Posteriormente se extrae la transformada discreta de Fourier mediante la técnica de windowing, la cual es la característica de la señal, lo que permite entrenar un clasificador random forest y k-nearest neighbors para posteriormente evaluar y validar el rendimiento del sistema de clasificación. La implementación del sistema se realiza usando elementos comerciales y la validación del sistema consiste en cruzar el resultado de la clasificación con los reportes técnicos que ratifican el estado de los compresores en cuestión. (Texto tomado de la fuente) |
| publishDate |
2023 |
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2023-10-26T02:25:08Z |
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2023-10-26T02:25:08Z |
| dc.date.issued.none.fl_str_mv |
2023-10-24 |
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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/84834 |
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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/84834 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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Wahyu Caesarendra, Buyung Kosasih, Anh Kiet Tieu, Hongtao Zhu, Craig A.S. Moodie, and Qiang Zhu. Acoustic emission-based condition monitoring methods: Review and application for low speed slew bearing. Mechanical Systems and Signal Processing, 72-73:134 – 159, 2016. T. d. M. Prego, A. A. de Lima, S. L. Netto, and E. A. B. da Silva. Audio anomaly detection on rotating machinery using image signal processing. In 2016 IEEE 7th Latin American Symposium on Circuits Systems (LASCAS), pages 207–210, Feb 2016. Paulo Antonio Delgado-Arredondo, Daniel Morinigo-Sotelo, Roque Alfredo Osornio- Rios, Juan Gabriel Avina-Cervantes, Horacio Rostro-Gonzalez, and Rene de Jesus Romero-Troncoso. Methodology for fault detection in induction motors via sound and vibration signals. Mechanical Systems and Signal Processing, 83:568–589, 2017. Adam Glowacz. Fault diagnosis of single-phase induction motor based on acoustic signals. Mechanical Systems and Signal Processing, 117:65–80, 2019. Adam Glowacz, Witold Glowacz, Zygfryd Glowacz, and Jaroslaw Kozik. Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals. Measurement, 113:1 – 9, 2018. Patricia Henriquez, Jesus B. Alonso, Miguel A. Ferrer, and Carlos M. Travieso. Re- view of automatic fault diagnosis systems using audio and vibration signals. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44(5):642–652, 2014. Ram Bihari Sharma and Anand Parey. Condition monitoring of gearbox using experi- mental investigation of acoustic emission technique. Procedia Engineering, 173:1575 – 1579, 2017. Plasticity and Impact Mechanics. KATSUHIKO SHIBATA, ATSUSHI TAKAHASHI, and TAKUYA SHIRAI. Fault diag- nosis of rotating machinery through visualisation of sound signals. Mechanical Systems and Signal Processing, 14(2):229 – 241, 2000. N Tandon and A Choudhury. A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribology International, 32(8):469 – 480, 1999. R. Vilela, J. C. Metrolho, and J. C. Cardoso. Machine and industrial monitorization system by analysis of acoustic signatures. In Proceedings of the 12th IEEE Mediterra- nean Electrotechnical Conference (IEEE Cat. No.04CH37521), volume 1, pages 277–279 Vol.1, May 2004. S. K. Yadav, K. Tyagi, B. Shah, and P. K. Kalra. Audio signature-based condition monitoring of internal combustion engine using fft and correlation approach. IEEE Transactions on Instrumentation and Measurement, 60(4):1217–1226, April 2011. Paulo Antonio Delgado-Arredondo, Daniel Morinigo-Sotelo, Roque Alfredo Osornio- Rios, Juan Gabriel Avina-Cervantes, Horacio Rostro-Gonzalez, and Rene de Jesus Romero-Troncoso. Methodology for fault detection in induction motors via sound and vibration signals. Mechanical Systems and Signal Processing, 83:568 – 589, 2017. Adam Glowacz. Acoustic based fault diagnosis of three-phase induction motor. Applied Acoustics, 137:82–89, 2018. Ahmad Qurthobi, Rytis Maskeli ̄unas, and Robertas Damaˇseviˇcius. Detection of mecha- nical failures in industrial machines using overlapping acoustic anomalies: A systematic literature review. Sensors, 22(10), 2022. P.J. Tavner. Review of condition monitoring of rotating electrical machines. IET Electric Power Applications, 2:215–247(32), July 2008. Xiangwei Liu, Deli Pei, Gabriel Lodewijks, Zhangyan Zhao, and Jie Mei. Acoustic signal based fault detection on belt conveyor idlers using machine learning. Advanced Powder Technology, 31(7):2689–2698, 2020. Md Junayed Hasan, M.M. Manjurul Islam, and Jong-Myon Kim. Acoustic spectral imaging and transfer learning for reliable bearing fault diagnosis under variable speed conditions. Measurement, 138:620–631, 2019. Shengnan Tang, Yong Zhu, and Shouqi Yuan. An adaptive deep learning model towards fault diagnosis of hydraulic piston pump using pressure signal. Engineering Failure Analysis, 138:106300, 2022. Jiachi Yao, Chao Liu, Keyu Song, Xiaochen Zhang, and Dongxiang Jiang. Fault de- tection of complex planetary gearbox using acoustic signals. Measurement, 178:109428, 2021 Vamsi Inturi, Sabareesh G R, and Vaibhav Sharma. Integrated vibro-acoustic analysis and empirical mode decomposition for fault diagnosis of gears in a wind turbine. Pro- cedia Structural Integrity, 14:937–944, 2019. SICE 2018 (2nd International Conference on Structural Integrity and Exhibition 2018). Adam Glowacz, Ryszard Tadeusiewicz, Stanislaw Legutko, Wahyu Caesarendra, Muhammad Irfan, Hui Liu, Frantisek Brumercik, Miroslav Gutten, Maciej Sulowicz, Jose Alfonso Antonino Daviu, Thompson Sarkodie-Gyan, Pawel Fracz, Anil Kumar, and Jiawei Xiang. Fault diagnosis of angle grinders and electric impact drills using acoustic signals. Applied Acoustics, 179:108070, 2021. Ayhan Altinors, Ferhat Yol, and Orhan Yaman. A sound based method for fault detec- tion with statistical feature extraction in uav motors. Applied Acoustics, 183:108325, 2021. Chen Peng, ZhiPeng Li, Minjing Yang, Minrui Fei, and Yulong Wang. An audio-based intelligent fault diagnosis method for belt conveyor rollers in sand carrier. Control Engineering Practice, 105:104650, 2020. B. Rubhini and P. Vanaja Ranjan. Machine condition monitoring using audio signature analysis. In 2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN), pages 1–6, 2017. Xin Wang, Dongxing Mao, and Xiaodong Li. Bearing fault diagnosis based on vibro- acoustic data fusion and 1d-cnn network. Measurement, 173:108518, 2021. Ning Dayong, Sun Changle, Gong Yongjun, Zhang Zengmeng, and Hou Jiaoyi. Extrac- tion of fault component from abnormal sound in diesel engines using acoustic signals. Mechanical Systems and Signal Processing, 75:544–555, 2016. Emin Germen, Murat Basaran, and Mehmet Fidan. Sound based induction motor fault diagnosis using kohonen self-organizing map. Mechanical Systems and Signal Processing, 01 2014. Dingcheng Zhang, Edward Stewart, Mani Entezami, Clive Roberts, and Dejie Yu. Inte- lligent acoustic-based fault diagnosis of roller bearings using a deep graph convolutional network. Measurement, 156:107585, 2020. Anand Parey and Amandeep Singh. Gearbox fault diagnosis using acoustic signals, con- tinuous wavelet transform and adaptive neuro-fuzzy inference system. Applied Acous- tics, 147:133–140, 2019. Special Issue on Design and Modelling of Mechanical Systems conference CMSM’2017. Mingjin Yang, Wenju Zhou, and Tianxiang Song. Audio-based fault diagnosis for belt conveyor rollers. Neurocomputing, 397:447–456, 2020. Nilesh W. Nirwan and Hardik B. Ramani. Condition monitoring and fault detection in roller bearing used in rolling mill by acoustic emission and vibration analysis. Materials Today: Proceedings, 51:344–354, 2022. CMAE’21. KATSUHIKO SHIBATA, ATSUSHI TAKAHASHI, and TAKUYA SHIRAI. Fault diag- nosis of rotating machinery through visualisation of sound signals. Mechanical Systems and Signal Processing, 14(2):229–241, 2000. R. Vilela, J.C. Metrolho, and J.C. Cardoso. Machine and industrial monitorization system by analysis of acoustic signatures. In Proceedings of the 12th IEEE Mediterra- nean Electrotechnical Conference (IEEE Cat. No.04CH37521), volume 1, pages 277–279 Vol.1, 2004. N. BAYDAR and A. BALL. Detection of gear failures via vibration and acoustic signals using wavelet transform. Mechanical Systems and Signal Processing, 17(4):787–804, 2003. MICHAEL L. FUGATE, HOON SOHN, and CHARLES R. FARRAR. Vibration-based damage detection using statistical process control. Mechanical Systems and Signal Pro- cessing, 15(4):707–721, 2001. Wenbo Lu, Weikang Jiang, Guoqing Yuan, and Li Yan. A gearbox fault diagnosis scheme based on near-field acoustic holography and spatial distribution features of sound field. Journal of Sound and Vibration, 332(10):2593–2610, 2013. Andrew K.S. Jardine, Daming Lin, and Dragan Banjevic. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7):1483 – 1510, 2006. Aleksi Sepp ̈anen et al. Utilizing acoustic measurements in equipment condition moni- toring. 2016. F. Keddar, A. Kaddour, and A. Maache. Analysis of the sound field emitted by a doubly salient switched reluctance motor using acoustic intensity measurement. In IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics, pages 1453–1458, Nov 2006. Xiang Duanqi, Wang Zheng, and Chen Jinjing. Acoustic design of an anechoic chamber. Applied Acoustics, 29(2):139 – 149, 1990. Ruonan Liu, Boyuan Yang, Enrico Zio, and Xuefeng Chen. Artificial intelligence for fault diagnosis of rotating machinery: A review. Mechanical Systems and Signal Processing, 108:33 – 47, 2018. Barbara Kitchenham, O. Pearl Brereton, David Budgen, Mark Turner, John Bailey, and Stephen Linkman. Systematic literature reviews in software engineering – a systema- tic literature review. Information and Software Technology, 51(1):7–15, 2009. Special Section - Most Cited Articles in 2002 and Regular Research Papers. Ian Sutton. Chapter 2 - maintenance. In Ian Sutton, editor, Plant Design and Operations (Second Edition), pages 25 – 45. Gulf Professional Publishing, second edition edition, 2017. John Semmlow. Chapter 2 - signal analysis in the time domain. In John Semmlow, editor, Circuits, Signals and Systems for Bioengineers (Third Edition), Biomedical En- gineering, pages 51 – 110. Academic Press, third edition edition, 2018. Núñez-Pérez Ricardo Francisco. The tendency of the crest factor helps detect nas- cent events; electronic circuit, software and applications to signals from diverse fields. Ingeniería Investigación y Tecnología, 15(1):63 – 81, 2014. John Semmlow. Chapter 3 - signal analysis in the frequency domain: The fourier series and the fourier transformation. In John Semmlow, editor, Circuits, Signals and Systems for Bioengineers (Third Edition), Biomedical Engineering, pages 111 – 168. Academic Press, third edition edition, 2018. A.V. Oppenheim, A.S. Willsky, S.H. Nawab, and G.M. Hern ́andez. Se ̃nales y sistemas. Pearson Educaci ́on, 1998. Wim van Drongelen. Chapter 21 - wavelet analysis: Time domain properties. In Wim van Drongelen, editor, Signal Processing for Neuroscientists (Second Edition), pages 401 – 423. Academic Press, second edition edition, 2018. Amani Yahyaoui’s, Imene Yahyaoui, and Nejat Yumu ̧sak. 13 - machine learning techni- ques for data classification. In Imene Yahyaoui, editor, Advances in Renewable Energies and Power Technologies, pages 441 – 450. Elsevier, 2018. Matthias Mauch and Simon Dixon. Pyin: A fundamental frequency estimator using pro- babilistic threshold distributions. In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 659–663, 2014. Marshall Long. 7 - sound and solid surfaces. In Marshall Long, editor, Architectural Acoustics (Second Edition), pages 259–312. Academic Press, Boston, second edition edition, 2014. Leo Breiman. Random forests. Machine Learning, 45(1):5–32, 2001. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blon- del, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830, 2011. Marco Jeub, Christian Herglotz, Christoph Nelke, Christophe Beaugeant, and Peter Vary. Noise reduction for dual-microphone mobile phones exploiting power level diffe- rences. In 2012 IEEE International Conference on Acoustics, Speech and Signal Pro- cessing (ICASSP), pages 1693–1696, 2012. |
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Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Camargo Bareño, Carlos Iván5f1800f640feba8ff0b07ec5c25757f2Escobar Mafla, Lennin Edmundoaa9ff5ad665be0c7f562af131e550556Computación Científica0000-0002-5676-6019LEscobar2023-10-26T02:25:08Z2023-10-26T02:25:08Z2023-10-24https://repositorio.unal.edu.co/handle/unal/84834Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/Este documento presenta el diseño, implementación y validación de herramienta de diagnostico temprano de motores eléctricos basado en audio para compresores de referencia NC4AV80ALR de la marca SAMSUNG, los cuales se encuentran en neveras. El diseño se basa en un análisis preliminar de la señal acústica emitida por el compresor en campo cercano mediante el uso de un micrófono, con el fin de seleccionar el punto en el espacio que presente las mejores características en pro de la calidad en la toma de las muestras, para esto, se analizan características como el valor RMS, frecuencia de roll-off y el centroide espectral. Como siguiente paso se crea un conjunto de datos de audio que consta de 25 compresores distribuidos equitativamente en 5 clases de las cuales 2 clases pertenecen a compresores que operan dentro de sus parámetros normales y las 3 clases restantes provienen de compresores que presentan fallas en su funcionamiento. Posteriormente se extrae la transformada discreta de Fourier mediante la técnica de windowing, la cual es la característica de la señal, lo que permite entrenar un clasificador random forest y k-nearest neighbors para posteriormente evaluar y validar el rendimiento del sistema de clasificación. La implementación del sistema se realiza usando elementos comerciales y la validación del sistema consiste en cruzar el resultado de la clasificación con los reportes técnicos que ratifican el estado de los compresores en cuestión. (Texto tomado de la fuente)This document presents the design, implementation and validation of an audio based tool for early diagnosis of electric motors used in compressors NC4AV80ALR of SAMSUNG which are found in refrigerators of the same brand. The proposed design consists in a preliminary analysis of the acoustic signal emitted by the compressor in a near field captured using a microphone in order to select the spot with the best characteristics in terms of sample quality. The features taken into account are the RMS value, roll-off frequency and spectral centroid. After the samples were taken we built a dataset with the data of 25 different compressors equally distributed in 5 classes from which 2 of them correspond to compressors running under normal conditions and the 3 remaining came from compressors with malfunctions. We make the fourier transform and with that data we trained some random forests and k-nearest neighbors classifiers and then we evaluate and validate the performance of this training. The system implementation is made using commercially available elements and the validation consists in relating the results from the classification with the technical reports of the compressors that confirm said state.MaestríaMagíster en Ingeniería - Ingeniería ElectrónicaSe propone una metodología en el desarrollo de este trabajo.Diagnóstico de fallas en compresores basado en audioxv, 124 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería ElectrónicaFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computaciónElectrodomésticosElectricidad-aparatos e instrumentosMotores eléctricosHousehold appliances, electricElectric apparatus and appliancesElectric motorsFallaDiagnósticoacústicoFaultDiagnosisAcousticAudioDiseño, implementación y validación de herramienta de diagnóstico temprano de motores eléctricos basado en audio.Design, implementation, and validation of an early diagnosis tool for electric motors based on audio.Trabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMWahyu Caesarendra, Buyung Kosasih, Anh Kiet Tieu, Hongtao Zhu, Craig A.S. Moodie, and Qiang Zhu. Acoustic emission-based condition monitoring methods: Review and application for low speed slew bearing. Mechanical Systems and Signal Processing, 72-73:134 – 159, 2016.T. d. M. Prego, A. A. de Lima, S. L. Netto, and E. A. B. da Silva. Audio anomaly detection on rotating machinery using image signal processing. In 2016 IEEE 7th Latin American Symposium on Circuits Systems (LASCAS), pages 207–210, Feb 2016.Paulo Antonio Delgado-Arredondo, Daniel Morinigo-Sotelo, Roque Alfredo Osornio- Rios, Juan Gabriel Avina-Cervantes, Horacio Rostro-Gonzalez, and Rene de Jesus Romero-Troncoso. Methodology for fault detection in induction motors via sound and vibration signals. Mechanical Systems and Signal Processing, 83:568–589, 2017.Adam Glowacz. Fault diagnosis of single-phase induction motor based on acoustic signals. Mechanical Systems and Signal Processing, 117:65–80, 2019.Adam Glowacz, Witold Glowacz, Zygfryd Glowacz, and Jaroslaw Kozik. Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals. Measurement, 113:1 – 9, 2018.Patricia Henriquez, Jesus B. Alonso, Miguel A. Ferrer, and Carlos M. Travieso. Re- view of automatic fault diagnosis systems using audio and vibration signals. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44(5):642–652, 2014.Ram Bihari Sharma and Anand Parey. Condition monitoring of gearbox using experi- mental investigation of acoustic emission technique. Procedia Engineering, 173:1575 – 1579, 2017. Plasticity and Impact Mechanics.KATSUHIKO SHIBATA, ATSUSHI TAKAHASHI, and TAKUYA SHIRAI. Fault diag- nosis of rotating machinery through visualisation of sound signals. Mechanical Systems and Signal Processing, 14(2):229 – 241, 2000.N Tandon and A Choudhury. A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribology International, 32(8):469 – 480, 1999.R. Vilela, J. C. 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In 2012 IEEE International Conference on Acoustics, Speech and Signal Pro- cessing (ICASSP), pages 1693–1696, 2012.Diseño, implementación y validación de herramienta de diagnóstico temprano de motores eléctricos basado en audioLennin Edmundo Escobar MaflaEstudiantesInvestigadoresMaestrosLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/84834/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1085254913.2023.pdf1085254913.2023.pdfTesis de Maestría en Ingeniería - Ingeniería Electrónicaapplication/pdf15365616https://repositorio.unal.edu.co/bitstream/unal/84834/2/1085254913.2023.pdf1467a140837cabd34eba34bd84f60a0dMD52THUMBNAIL1085254913.2023.pdf.jpg1085254913.2023.pdf.jpgGenerated Thumbnailimage/jpeg4683https://repositorio.unal.edu.co/bitstream/unal/84834/3/1085254913.2023.pdf.jpgf2fdc1e6ecde5e73275dd8b132f6fdf7MD53unal/84834oai:repositorio.unal.edu.co:unal/848342023-10-25 23:04:14.812Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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