Sistema de Diagnóstico Distribuido de Motores de Inducción basado en Redes Inalámbricas de Sensores y Protocolo ZigBee

La inclusión de las redes inalámbricas de sensores y tecnologías IoT (Internet of Things) en ambientes industriales busca el monitoreo y registro autónomo de una mayor cantidad de variables del proceso industrial con una alta confiabilidad y resiliencia, además, procuran realizar un análisis previó...

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Autores:
Caballero Peña, Jairo Andrés
Tipo de recurso:
Work document
Fecha de publicación:
2020
Institución:
Universidad Nacional de Colombia
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Universidad Nacional de Colombia
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spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/75656
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/75656
Palabra clave:
Ingeniería y operaciones afines::Otras ramas de la ingeniería
Distributed Analysis
Fault Diagnosis
Induction Motor
Motor-Current Signature Analysis
Stator Current
Wireless Sensor Networks
ZigBee
Análisis distribuido
Corriente de estator
Diagnóstico de fallas
Motor-Current Signature Analysis
Motor de Inducción
Redes inalámbricas de sensores
ZigBee
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openAccess
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Atribución-NoComercial-SinDerivadas 4.0 Internacional
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oai_identifier_str oai:repositorio.unal.edu.co:unal/75656
network_acronym_str UNACIONAL2
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repository_id_str
dc.title.spa.fl_str_mv Sistema de Diagnóstico Distribuido de Motores de Inducción basado en Redes Inalámbricas de Sensores y Protocolo ZigBee
title Sistema de Diagnóstico Distribuido de Motores de Inducción basado en Redes Inalámbricas de Sensores y Protocolo ZigBee
spellingShingle Sistema de Diagnóstico Distribuido de Motores de Inducción basado en Redes Inalámbricas de Sensores y Protocolo ZigBee
Ingeniería y operaciones afines::Otras ramas de la ingeniería
Distributed Analysis
Fault Diagnosis
Induction Motor
Motor-Current Signature Analysis
Stator Current
Wireless Sensor Networks
ZigBee
Análisis distribuido
Corriente de estator
Diagnóstico de fallas
Motor-Current Signature Analysis
Motor de Inducción
Redes inalámbricas de sensores
ZigBee
title_short Sistema de Diagnóstico Distribuido de Motores de Inducción basado en Redes Inalámbricas de Sensores y Protocolo ZigBee
title_full Sistema de Diagnóstico Distribuido de Motores de Inducción basado en Redes Inalámbricas de Sensores y Protocolo ZigBee
title_fullStr Sistema de Diagnóstico Distribuido de Motores de Inducción basado en Redes Inalámbricas de Sensores y Protocolo ZigBee
title_full_unstemmed Sistema de Diagnóstico Distribuido de Motores de Inducción basado en Redes Inalámbricas de Sensores y Protocolo ZigBee
title_sort Sistema de Diagnóstico Distribuido de Motores de Inducción basado en Redes Inalámbricas de Sensores y Protocolo ZigBee
dc.creator.fl_str_mv Caballero Peña, Jairo Andrés
dc.contributor.advisor.spa.fl_str_mv Rosero García, Javier Alveiro
dc.contributor.author.spa.fl_str_mv Caballero Peña, Jairo Andrés
dc.subject.ddc.spa.fl_str_mv Ingeniería y operaciones afines::Otras ramas de la ingeniería
topic Ingeniería y operaciones afines::Otras ramas de la ingeniería
Distributed Analysis
Fault Diagnosis
Induction Motor
Motor-Current Signature Analysis
Stator Current
Wireless Sensor Networks
ZigBee
Análisis distribuido
Corriente de estator
Diagnóstico de fallas
Motor-Current Signature Analysis
Motor de Inducción
Redes inalámbricas de sensores
ZigBee
dc.subject.proposal.eng.fl_str_mv Distributed Analysis
Fault Diagnosis
Induction Motor
Motor-Current Signature Analysis
Stator Current
Wireless Sensor Networks
ZigBee
dc.subject.proposal.spa.fl_str_mv Análisis distribuido
Corriente de estator
Diagnóstico de fallas
Motor-Current Signature Analysis
Motor de Inducción
Redes inalámbricas de sensores
ZigBee
description La inclusión de las redes inalámbricas de sensores y tecnologías IoT (Internet of Things) en ambientes industriales busca el monitoreo y registro autónomo de una mayor cantidad de variables del proceso industrial con una alta confiabilidad y resiliencia, además, procuran realizar un análisis previó para obtener parámetros de las señales que puedan dar a conocer una mejor descripción del estado del sistema y su condición de operación. Esto permite reducir el consumo de energía al disminuir la transmisión de datos netos medidos con paquetes hasta mil veces más largas que un parámetro calculado desde el sensor hacia los centros de control. La finalidad del monitoreo propuesto es el análisis para la identificación de anomalías que puedan afectar la disponibilidad de la planta o incrementar los costos de producción, y mejorar los procesos de mantenimiento. En este proyecto se desarrolló un sistema de monitoreo y diagnóstico remoto basado en una red de sensores cuyos nodos remotos se encarguen de la recolección de datos y su posterior análisis para la identificación de anomalías que representen fallas críticas para el proceso o sistema industrial. El sistema propuesto se enfocó en el diagnóstico de falla de motores de inducción debido a que representan el mayor porcentaje de equipos en aplicaciones industriales. El proyecto se limitó a la identificación de falla entre espiras (2, 4 y 6 espiras), como un antecedente de fallas críticas, corto circuito fase-fase y corto circuito fase-tierra al presentarse como un deterioro del aislamiento. Se empleo el método de análisis de corriente de estator (MCSA). El nodo remoto inteligente se implementó con MCU LPCXpresso54114 con conexión a una red inalámbrica de sensores basada en protocolo ZigBee mediante tarjetas de comunicación XBee. El nodo concentrador (gateway) está compuesto de una tarjeta Raspberrry PI con comunicación mediante protocolo HTTP y formato JSON (PI Web API) a la base de datos del sistema de monitoreo industrial PI System El diagnóstico se ejecuta de manera remota por medio de un análisis preliminar para el cálculo de indicadores de falla y luego mediante SVM (Support Vector Machine) se clasifican los datos en comportamientos conocidos de condiciones de falla. Se plantearon indicadores basados en la medición neta de las corrientes, FFT (Fast Fourier Transform) y DWT (Discrete Wavelet Transform). Se realizó validación en laboratorio de la clasificación en tiempo real de fallas entre espiras aplicadas a un motor de inducción tipo jaula de ardilla, comparando diferentes configuraciones del diagnóstico, del análisis para la extracción de indicadores y de los indicadores de falla empleados; permitiendo plantear mejoras para la reducción de los porcentajes de error por falsa detección de falla, o no detección de falla. Estos avances finalmente se traducen a incrementar la confiabilidad del diagnóstico, la observabilidad de la falla, la diferenciación entre condiciones de falla, la precisión de la clasificación, la tolerancia a transitorios, sensibilidad, entre otros.
publishDate 2020
dc.date.accessioned.spa.fl_str_mv 2020-02-20T15:58:17Z
dc.date.available.spa.fl_str_mv 2020-02-20T15:58:17Z
dc.date.issued.spa.fl_str_mv 2020-01-10
dc.type.spa.fl_str_mv Documento de trabajo
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dc.language.iso.spa.fl_str_mv spa
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dc.relation.references.spa.fl_str_mv [1] J. P. Amaro, F. J. T. E. Ferreira, R. Cortesão, N. Vinagre, and R. P. Bras, “Low cost wireless sensor network for in-field operation monitoring of induction motors,” Proc. IEEE Int. Conf. Ind. Technol., pp. 1044–1049, 2010. [2] M. Bordasch, C. Brand, and P. Gohner, “Fault-based identification and inspection of fault developments to enhance availability in industrial automation systems,” in 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA), 2015, vol. 2015–Octob, pp. 1–8. [3] L. Hou and N. W. Bergmann, “Novel industrial wireless sensor networks for machine condition monitoring and fault diagnosis,” IEEE Trans. Instrum. Meas., vol. 61, no. 10, pp. 2787–2798, 2012. [4] F. J. T. E. Ferreira, G. Baoming, and A. T. de Almeida, “Reliability and operation of high-efficiency induction motors,” 2015 IEEE/IAS 51st Ind. Commer. Power Syst. Tech. Conf., pp. 1–13, 2015. [5] A. Gandhi, T. Corrigan, and L. Parsa, “Recent Advances in Modeling and Online Detection of Stator Interturn Faults in Electrical Motors,” IEEE Transactions on Industrial Electronics, vol. 58, no. 5. pp. 1564–1575, 2011. [6] S. Bindu and V. V Thomas, “Diagnoses of internal faults of three phase squirrel cage induction motor - A review,” Advances in Energy Conversion Technologies (ICAECT), 2014 International Conference on. pp. 48–54, 2014. [7] S. Nandi, H. A. Toliyat, and X. Li, “Condition monitoring and fault diagnosis of electrical motors - A review,” IEEE Trans. Energy Convers., vol. 20, no. 4, pp. 719–729, 2005. [8] A. Sapena-Baño, J. Perez-Cruz, M. Pineda-Sanchez, R. Puche-Panadero, J. Roger-Folch, M. Riera-Guasp, and J. Martinez-Roman, “Condition monitoring of electrical machines using low computing power devices,” 2014 Int. Conf. Electr. Mach., pp. 1516–1522, 2014. [9] R. Windings, “In-service monitoring of stator and rotor windings,” pp. 389–437, 2014. [10] İ. Aydın, M. Karaköse, and E. Akın, “Combined intelligent methods based on wireless sensor networks for condition monitoring and fault diagnosis,” J. Intell. Manuf., vol. 26, no. 4, pp. 717–729, Aug. 2013. [11] L. Hou and N. W. Bergmann, “Induction motor fault diagnosis using industrial wireless sensor networks and Dempster-Shafer classifier fusion,” in IECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society, 2011, pp. 2992–2997. [12] N. W. Bergmann and L.-Q. Hou, “Energy Efficient Machine Condition Monitoring Using Wireless Sensor Networks,” 2014 Int. Conf. Wirel. Commun. Sens. Netw., pp. 285–290, 2014. [13] L. Hou and N. W. Bergmann, “Induction motor condition monitoring using industrial wireless sensor networks,” 2010 Sixth Int. Conf. Intell. Sensors, Sens. Networks Inf. Process., pp. 49–54, 2010. [14] S. H. Kia, H. Henao, S. Member, and G. Capolino, “Efficient Digital Signal Processing Techniques for Induction Machines Fault Diagnosis,” Electr. Mach. Des. Control Diagnosis (WEMDCD), 2013 IEEE Work., pp. 232–246, 2013. [15] H. Henao, G.-A. Capolino, M. Fernandez-Cabanas, F. Filippetti, C. Bruzzese, E. Strangas, R. Pusca, J. Estima, M. Riera-Guasp, and S. Hedayati-Kia, “Trends in Fault Diagnosis for Electrical Machines: A Review of Diagnostic Techniques,” IEEE Ind. Electron. Mag., vol. 8, no. 2, pp. 31–42, 2014. [16] M. Riera-Guasp, J. A. Antonino-Daviu, and G.-A. Capolino, “Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art,” IEEE Trans. Ind. Electron., vol. 62, no. 3, pp. 1746–1759, 2015. [17] S. Das, P. Purkait, D. Dey, and S. Chakravorti, “Monitoring of inter-turn insulation failure in induction motor using advanced signal and data processing tools,” IEEE Trans. Dielectr. Electr. Insul., vol. 18, no. 5, pp. 746–751, 2002. [18] S. Choi, B. Akin, M. M. Rahimian, and H. A. Toliyat, “Performance-Oriented Electric Motors Diagnostics in Modern Energy Conversion Systems,” IEEE Transactions on Industrial Electronics, vol. 59, no. 2. pp. 1266–1277, 2012. [19] S. Choi, E. Pazouki, J. Baek, and H. R. Bahrami, “Iterative Condition Monitoring and Fault Diagnosis Scheme of Electric Motor for Harsh Industrial Application,” IEEE Transactions on Industrial Electronics, vol. 62, no. 3. pp. 1760–1769, 2015. [20] S. Cheng, S. Member, P. Zhang, and T. G. Habetler, “An Impedance Identification Approach to Sensitive Detection and Location of Stator Turn-to-Turn Faults in a Closed-Loop Multiple-Motor Drive,” IEEE Trans. Ind. Electron., vol. 58, no. 5, pp. 1545–1554, 2011. [21] Kyusung Kim, A. G. Parlos, and R. M. Bharadwaj, “Sensorless fault diagnosis of induction motors,” IEEE Trans. Ind. Electron., vol. 50, no. 5, pp. 1038–1051, Oct. 2003. [22] S. H. Kia, H. Henao, and G. A. Capolino, “A comparative study of acoustic, vibration and stator current signatures for gear tooth fault diagnosis,” Proc. - 2012 20th Int. Conf. Electr. Mach. ICEM 2012, no. 1, pp. 1514–1519, 2012. [23] G. A. Capolino, J. A. Antonino-Daviu, and M. Riera-Guasp, “Modern diagnostics techniques for electrical machines, power electronics, and drives,” IEEE Trans. Ind. Electron., vol. 62, no. 3, p. 8, 2015. [24] E. T. Esfahani, S. Wang, and V. Sundararajan, “Multisensor wireless system for eccentricity and bearing fault detection in induction motors,” IEEE/ASME Trans. Mechatronics, vol. 19, no. 3, pp. 818–826, Jun. 2014. [25] A. Schmitt, Helder Luiz, Silva L.R.B, Scalassara, P.R.,Goedtel, “Bearing Fault Detection Using Relative Entropy of Wavelet Components And Artificial Neural Networks,” pp. 538–543, 2013. [26] J. Pons-Llinares, J. A. Antonino-Daviu, M. Riera-Guasp, S. Bin Lee, T. J. Kang, and C. Yang, “Advanced induction motor rotor fault diagnosis via continuous and discrete time-frequency tools,” IEEE Trans. Ind. Electron., vol. 62, no. 3, pp. 1791–1802, 2015. [27] G. Jagadanand and F. L. Dias, “ARM based induction motor fault detection using wavelet and support vector machine,” Signal Processing, Informatics, Communication and Energy Systems (SPICES), 2015 IEEE International Conference on. pp. 1–4, 2015. [28] D. Zurita, M. Delgado, J. A. Ortega, and L. Romeral, “Intelligent sensor based on acoustic emission analysis applied to gear fault diagnosis,” Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED), 2013 9th IEEE International Symposium on. pp. 169–176, 2013. [29] M. Delgado Prieto, D. Zurita Millan, W. Wang, A. Machado Ortiz, J. A. Ortega Redondo, and L. Romeral Martinez, “Self-powered wireless sensor applied to gear diagnosis based on acoustic emission,” IEEE Trans. Instrum. Meas., vol. 65, no. 1, pp. 15–24, 2016. [30] O. Yaman, I. Aydin, M. Karaköse, and E. Akinn, “Wireless sensor network based fault diagnosis approaches,” Signal Processing and Communications Applications Conference (SIU), 2013 21st. pp. 1–4, 2013. [31] P. S. Barendse, B. Herndler, M. A. Khan, and P. Pillay, “The application of wavelets for the detection of inter-turn faults in induction machines,” 2009 IEEE Int. Electr. Mach. Drives Conf. IEMDC ’09, pp. 1401–1407, 2009. [32] N. R. Devi, S. A. Gafoor, and P. V. R. Rao, “Wavelet ANN based stator internal faults protection scheme for 3-phase induction motor,” Proc. 2010 5th IEEE Conf. Ind. Electron. Appl. ICIEA 2010, pp. 1457–1461, 2010. [33] N. Rama Devi, D. V. S. S. Siva Sarma, and P. V. Ramana Rao, “Detection of stator incipient faults and identification of faulty phase in three-phase induction motor – simulation and experimental verification,” IET Electr. Power Appl., vol. 9, no. 8, pp. 540–548, 2015. [34] M. A. S. K. Khan, T. S. Radwan, and M. A. Rahman, “Real-time implementation of wavelet packet transform-based diagnosis and protection of three-phase induction motors,” IEEE Trans. Energy Convers., vol. 22, no. 3, pp. 647–655, 2007. [35] J. A. Rosero, L. Romeral, J. A. Ortega, and E. Rosero, “Short-circuit detection by means of empirical mode decomposition and Wigner-Ville distribution for PMSM running under dynamic condition,” IEEE Trans. Ind. Electron., vol. 56, no. 11, pp. 4534–4547, 2009. [36] E. Elbouchikhi, V. Choqueuse, Y. Amirat, M. Benbouzid, and S. Turri, “An Efficient Hilbert-Huang Transform-based Bearing Faults Detection in Induction Machines,” IEEE Trans. Energy Convers., vol. 32, no. 2, pp. 1–1, 2017. [37] E. Elbouchikhi, V. Choqueuse, Y. Trachi, and M. Benbouzid, “Induction machine bearing faults detection based on Hilbert-Huang transform,” IEEE Int. Symp. Ind. Electron., vol. 2015–Septe, pp. 843–848, 2015. [38] M. Amarnath and I. R. Praveen Krishna, “Empirical mode decomposition of acoustic signals for diagnosis of faults in gears and rolling element bearings,” IET Sci. Meas. Technol., vol. 6, no. 4, p. 279, 2012. [39] G.-J. Feng, J. Gu, D. Zhen, M. Aliwan, F.-S. Gu, and A. D. Ball, “Implementation of envelope analysis on a wireless condition monitoring system for bearing fault diagnosis,” Int. J. Autom. Comput., vol. 12, no. 1, pp. 14–24, Feb. 2015. [40] V. C. M. N. Leite, J. G. Borges Da Silva, G. F. C. Veloso, L. E. Borges Da Silva, G. Lambert-Torres, E. L. Bonaldi, and L. E. De Lacerda De Oliveira, “Detection of localized bearing faults in induction machines by spectral kurtosis and envelope analysis of stator current,” IEEE Trans. Ind. Electron., vol. 62, no. 3, pp. 1855–1865, 2015. [41] D. He, R. Li, and J. Zhu, “Plastic Bearing Fault Diagnosis Based on a Two-Step Data Mining Approach,” IEEE Trans. Ind. Electron., vol. 60, no. 8, pp. 1–1, 2012. [42] S. Mallat, “Wavelet Bases,” in A Wavelet Tour of Signal Processing, Third., Elsevier Ltd, 2009, pp. 263–376. [43] H. Douglas, P. Pillay, and P. Barendse, “The detection of interturn stator faults in doubly-fed induction generators,” in Conference Record - IAS Annual Meeting (IEEE Industry Applications Society), 2005, vol. 2, pp. 1097–1102. [44] N. Laouti, S. Othman, M. Alamir, and N. Sheibat-Othman, “Combination of Model-based Observer and Support Vector Machines for Fault Detection of Wind Turbines,” Int. J. Autom. Comput., vol. 11, no. 3, pp. 274–287, Mar. 2015. [45] D. A. Asfani, M. H. Purnomo, and D. R. Sawitri, “Naïve Bayes classifier for Temporary short circuit fault detection in Stator Winding,” pp. 288–294, 2013. [46] H. O. Vishwakarma, K. S. Sajan, B. Maheshwari, and Y. D. Dhiman, “Intelligent bearing fault monitoring system using support vector machine and wavelet packet decomposition for induction motors,” Power and Advanced Control Engineering (ICPACE), 2015 International Conference on. pp. 339–343, 2015. [47] V. Vetiska, O. Hyncica, C. Ondrusek, and Z. Hadas, “Autonomous monitoring unit of fault condition with vibration energy harvester,” 2015 IEEE 15th Int. Conf. Environ. Electr. Eng. EEEIC 2015 - Conf. Proc., pp. 980–985, 2015. [48] J. Neuzil, O. Kreibich, and R. Smid, “A distributed fault detection system based on IWSN for machine condition monitoring,” IEEE Trans. Ind. Informatics, vol. 10, no. 2, pp. 1118–1123, 2014. [49] W. Ikram, S. L. Chen, T. Harvei, T. Olsen, E. Mikalsen, G. Svoen, S. Froystein, and B. Myhre, “Vibration-based wireless machine condition monitoring system,” Emerging Technology and Factory Automation (ETFA), 2014 IEEE. pp. 1–4, 2014. [50] F. Philipp, J. Martinez, M. Glesner, and A. Arkkio, “A smart wireless sensor for the diagnosis of broken bars in induction motors,” Proc. Bienn. Balt. Electron. Conf. BEC, no. 1, pp. 119–122, 2012. [51] University of California - Berkeley and Stanford University, “TinyOS Documentation Wiki.” [Online]. Available: http://tinyos.stanford.edu/tinyos-wiki/index.php/Main_Page. [Accessed: 18-Aug-2017]. [52] O. Kreibich, J. Neuzil, and R. Smid, “Quality-based multiple-sensor fusion in an industrial wireless sensor network for MCM,” IEEE Trans. Ind. Electron., vol. 61, no. 9, pp. 4903–4911, 2014. [53] G. M. Joksimovic and J. Penman, “The detection of inter-turn short circuits in the stator windings of operating motors,” IEEE Trans. Ind. Electron., vol. 47, no. 5, pp. 1078–1084, 2000. [54] M. A. Delgado Narváez, “Monitoreo y Diagnóstico de Electric Machine Drive Systems (EMDS),” Universidad Nacional de Colombia - Sede Bogotá, 2017. [55] G. G. Yen and K.-C. Lin, “Wavelet packet feature extraction for vibration monitoring,” IEEE Trans. Ind. Electron., vol. 47, no. 3, pp. 650–667, 2000. [56] I. Daubechies and W. Sweldens, “Factoring wavelet transforms into lifting steps,” J. Fourier Anal. Appl., vol. 4, no. 3, pp. 247–269, 1998. [57] F. L. I. A. Dias and G. Jagadanand, “ARM based wavelet transform implementation for embedded system applications,” pp. 122–126, 2014. [58] J. Antoni, “The spectral kurtosis: A useful tool for characterising non-stationary signals,” Mech. Syst. Signal Process., vol. 20, no. 2, pp. 282–307, 2006. [59] P. H. Nguyen and J. M. Kim, “Multifault diagnosis of rolling element bearings using a wavelet kurtogram and vector median-based feature analysis,” Shock Vib., vol. 2015, no. October 2016, 2015. [60] J. Antoni, “Fast computation of the kurtogram for the detection of transient faults,” Mech. Syst. Signal Process., vol. 21, no. 1, pp. 108–124, 2007. [61] G. A., J. Manuel, J.-L. Marty, and R. Munoz, “Implementation of the Discrete Wavelet Transform Used in the Calibration of the Enzymatic Biosensors,” Discret. Wavelet Transform. - Biomed. Appl., 2012. [62] G. R. Lee, R. Gommers, F. Waselewski, K. Wohlfahrt, and A. O’Leary, “PyWavelets: A Python package for wavelet analysis,” J. Open Source Softw., vol. 4, no. 36, p. 1237, 2019. [63] J. Vanderplas, “In-Depth: Support Vector Machines,” in Python Data Science Handbook, O’Reilly Media, 2016, p. 541. [64] L. Buitinck, G. Louppe, M. Blondel, F. Pedregosa, A. Mueller, O. Grisel, V. Niculae, P. Prettenhofer, A. Gramfort, J. Grobler, R. Layton, J. Vanderplas, A. Joly, B. Holt, and G. Varoquaux, “API design for machine learning software: experiences from the scikit-learn project,” ECML PKDD Workshop: Languages for Data Mining and Machine Learning, pp. 108–122, 2013. [65] G. Feng, A. Mustafa, J. X. Gu, D. Zhen, F. Gu, and A. D. Ball, “The real-time implementation of envelope analysis for bearing fault diagnosis based on wireless sensor network,” Automation and Computing (ICAC), 2013 19th International Conference on. pp. 1–6, 2013. [66] Maxim Integrated, “MAX291/MAX292/MAX295/MAX296 8th-Order, Lowpass, Switched-Capacitor Filters,” no. Rev 5., pp. 1–10. [67] D. Navarro, “SystemC Models : Blocks - Documentation,” Lyon Institute of Nanotechnologies - France. [Online]. Available: http://idea1inl.free.fr/IDEA1/links.html. [68] W. Du, F. Mieyeville, D. Navarro, and I. O. Connor, “IDEA1: A validated SystemC-based system-level design and simulation environment for wireless sensor networks,” Eurasip J. Wirel. Commun. Netw., vol. 2011, no. 1, pp. 1–20, 2011. [69] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, 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,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011. [70] J. A. Caballero Peña and J. A. Rosero, “Procedimiento: Medición de desempeño de motores bajo carga (motor + driver) con variador de velocidad regenerativo,” Bogotá D.C., Rev_A, 2018.
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spelling Atribución-NoComercial-SinDerivadas 4.0 InternacionalDerechos reservados - Universidad Nacional de ColombiaAcceso abiertohttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Rosero García, Javier Alveiro1aa45eb1-84b4-4f17-ae67-d36f493e55da-1Caballero Peña, Jairo Andrésb33f2cd3-2617-4d38-899b-34c69612704f2020-02-20T15:58:17Z2020-02-20T15:58:17Z2020-01-10https://repositorio.unal.edu.co/handle/unal/75656La inclusión de las redes inalámbricas de sensores y tecnologías IoT (Internet of Things) en ambientes industriales busca el monitoreo y registro autónomo de una mayor cantidad de variables del proceso industrial con una alta confiabilidad y resiliencia, además, procuran realizar un análisis previó para obtener parámetros de las señales que puedan dar a conocer una mejor descripción del estado del sistema y su condición de operación. Esto permite reducir el consumo de energía al disminuir la transmisión de datos netos medidos con paquetes hasta mil veces más largas que un parámetro calculado desde el sensor hacia los centros de control. La finalidad del monitoreo propuesto es el análisis para la identificación de anomalías que puedan afectar la disponibilidad de la planta o incrementar los costos de producción, y mejorar los procesos de mantenimiento. En este proyecto se desarrolló un sistema de monitoreo y diagnóstico remoto basado en una red de sensores cuyos nodos remotos se encarguen de la recolección de datos y su posterior análisis para la identificación de anomalías que representen fallas críticas para el proceso o sistema industrial. El sistema propuesto se enfocó en el diagnóstico de falla de motores de inducción debido a que representan el mayor porcentaje de equipos en aplicaciones industriales. El proyecto se limitó a la identificación de falla entre espiras (2, 4 y 6 espiras), como un antecedente de fallas críticas, corto circuito fase-fase y corto circuito fase-tierra al presentarse como un deterioro del aislamiento. Se empleo el método de análisis de corriente de estator (MCSA). El nodo remoto inteligente se implementó con MCU LPCXpresso54114 con conexión a una red inalámbrica de sensores basada en protocolo ZigBee mediante tarjetas de comunicación XBee. El nodo concentrador (gateway) está compuesto de una tarjeta Raspberrry PI con comunicación mediante protocolo HTTP y formato JSON (PI Web API) a la base de datos del sistema de monitoreo industrial PI System El diagnóstico se ejecuta de manera remota por medio de un análisis preliminar para el cálculo de indicadores de falla y luego mediante SVM (Support Vector Machine) se clasifican los datos en comportamientos conocidos de condiciones de falla. Se plantearon indicadores basados en la medición neta de las corrientes, FFT (Fast Fourier Transform) y DWT (Discrete Wavelet Transform). Se realizó validación en laboratorio de la clasificación en tiempo real de fallas entre espiras aplicadas a un motor de inducción tipo jaula de ardilla, comparando diferentes configuraciones del diagnóstico, del análisis para la extracción de indicadores y de los indicadores de falla empleados; permitiendo plantear mejoras para la reducción de los porcentajes de error por falsa detección de falla, o no detección de falla. Estos avances finalmente se traducen a incrementar la confiabilidad del diagnóstico, la observabilidad de la falla, la diferenciación entre condiciones de falla, la precisión de la clasificación, la tolerancia a transitorios, sensibilidad, entre otros.The inclusion of sensors wireless networks and Internet of Things (IoT) technologies in industrial environments seeks an autonomous monitoring and storage with high reliability and resilience of a greater number of industrial process variables, in addition, they attempt to perform a preliminary analysis to obtain parameters of the signals that can give a better description of system state and its operation condition. This allows reducing energy consumption by decreasing the transmission of raw data, a parameter calculated from the sensor to the control centers in change of a thousand times longer package. The purpose of the proposed monitoring is the analysis for the identification of anomalies that may affect the availability of the plant or increase production costs and improve maintenance processes. In this project, a remote fault diagnosis and monitoring system based on wireless sensor networks was developed whose remote nodes are responsible for data collection and analysis for the identification of anomalies over industrial process or system, previously to critical faults. The proposed system was focused on the induction motor fault diagnosis because these represent the highest percentage of equipment in industrial applications. This project was based on identify interturn faults (2, 4 and 6 turns) using Motor Current Signature Analysis (MCSA), because of the Interturn faults are produced by insulation deterioration and evolve in critical faults, phase to phase short-circuit and ground fault. The developed intelligent remote node was implemented with MCU LPCXpresso54114 with connection to a ZigBee protocol wireless sensor network through XBee communication module. The gateway node is a Raspberrry PI with communication through HTTP protocol and JSON format (PI Web API) to the PI System database (industrial monitoring system). The diagnosis is remotely executed, where a preliminary analysis is applied to calculate fault indicators. Then, with a SVM (Support Vector Machine), the data are classified in known behavior of fault conditions. Different fault indicators were proposed based on current measurement’s raw data, FFT (Fast Fourier Transform) and DWT (Discrete Wavelet Transform). Real time interturn fault classification was validated in laboratory with a squirrel cage induction motor comparing different settings and configuration of diagnosis, analysis for indicators extraction and testing diversified fault indicators. This allowed proposing improvements to reduce of error percentage by false detection or missing detection. The progress finally are reflected in increase the diagnosis reliability, the observability of the failure, the differentiation between fault conditions, classification accuracy, tolerance to transients, sensitivity, etc.Magíster en Ingeniería Eléctrica.Maestría168application/pdfspaIngeniería y operaciones afines::Otras ramas de la ingenieríaDistributed AnalysisFault DiagnosisInduction MotorMotor-Current Signature AnalysisStator CurrentWireless Sensor NetworksZigBeeAnálisis distribuidoCorriente de estatorDiagnóstico de fallasMotor-Current Signature AnalysisMotor de InducciónRedes inalámbricas de sensoresZigBeeSistema de Diagnóstico Distribuido de Motores de Inducción basado en Redes Inalámbricas de Sensores y Protocolo ZigBeeDocumento de trabajoinfo:eu-repo/semantics/workingPaperinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_8042Texthttp://purl.org/redcol/resource_type/WPDepartamento de Ingeniería Eléctrica y ElectrónicaUniversidad Nacional de Colombia - Sede Bogotá[1] J. P. Amaro, F. J. T. E. Ferreira, R. Cortesão, N. Vinagre, and R. P. Bras, “Low cost wireless sensor network for in-field operation monitoring of induction motors,” Proc. IEEE Int. Conf. Ind. Technol., pp. 1044–1049, 2010. [2] M. Bordasch, C. Brand, and P. Gohner, “Fault-based identification and inspection of fault developments to enhance availability in industrial automation systems,” in 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA), 2015, vol. 2015–Octob, pp. 1–8. [3] L. Hou and N. W. Bergmann, “Novel industrial wireless sensor networks for machine condition monitoring and fault diagnosis,” IEEE Trans. Instrum. Meas., vol. 61, no. 10, pp. 2787–2798, 2012. [4] F. J. T. E. Ferreira, G. Baoming, and A. T. de Almeida, “Reliability and operation of high-efficiency induction motors,” 2015 IEEE/IAS 51st Ind. Commer. Power Syst. Tech. Conf., pp. 1–13, 2015. [5] A. Gandhi, T. Corrigan, and L. Parsa, “Recent Advances in Modeling and Online Detection of Stator Interturn Faults in Electrical Motors,” IEEE Transactions on Industrial Electronics, vol. 58, no. 5. pp. 1564–1575, 2011. [6] S. Bindu and V. V Thomas, “Diagnoses of internal faults of three phase squirrel cage induction motor - A review,” Advances in Energy Conversion Technologies (ICAECT), 2014 International Conference on. pp. 48–54, 2014. [7] S. Nandi, H. A. Toliyat, and X. Li, “Condition monitoring and fault diagnosis of electrical motors - A review,” IEEE Trans. Energy Convers., vol. 20, no. 4, pp. 719–729, 2005. [8] A. Sapena-Baño, J. Perez-Cruz, M. Pineda-Sanchez, R. Puche-Panadero, J. Roger-Folch, M. Riera-Guasp, and J. Martinez-Roman, “Condition monitoring of electrical machines using low computing power devices,” 2014 Int. Conf. Electr. Mach., pp. 1516–1522, 2014. [9] R. Windings, “In-service monitoring of stator and rotor windings,” pp. 389–437, 2014. [10] İ. Aydın, M. Karaköse, and E. Akın, “Combined intelligent methods based on wireless sensor networks for condition monitoring and fault diagnosis,” J. Intell. Manuf., vol. 26, no. 4, pp. 717–729, Aug. 2013. [11] L. Hou and N. W. Bergmann, “Induction motor fault diagnosis using industrial wireless sensor networks and Dempster-Shafer classifier fusion,” in IECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society, 2011, pp. 2992–2997. [12] N. W. Bergmann and L.-Q. Hou, “Energy Efficient Machine Condition Monitoring Using Wireless Sensor Networks,” 2014 Int. Conf. Wirel. Commun. Sens. Netw., pp. 285–290, 2014. [13] L. Hou and N. W. Bergmann, “Induction motor condition monitoring using industrial wireless sensor networks,” 2010 Sixth Int. Conf. Intell. Sensors, Sens. Networks Inf. Process., pp. 49–54, 2010. [14] S. H. Kia, H. Henao, S. Member, and G. Capolino, “Efficient Digital Signal Processing Techniques for Induction Machines Fault Diagnosis,” Electr. Mach. Des. Control Diagnosis (WEMDCD), 2013 IEEE Work., pp. 232–246, 2013. [15] H. Henao, G.-A. Capolino, M. Fernandez-Cabanas, F. Filippetti, C. Bruzzese, E. Strangas, R. Pusca, J. Estima, M. Riera-Guasp, and S. Hedayati-Kia, “Trends in Fault Diagnosis for Electrical Machines: A Review of Diagnostic Techniques,” IEEE Ind. Electron. Mag., vol. 8, no. 2, pp. 31–42, 2014. [16] M. Riera-Guasp, J. A. Antonino-Daviu, and G.-A. Capolino, “Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art,” IEEE Trans. Ind. Electron., vol. 62, no. 3, pp. 1746–1759, 2015. [17] S. Das, P. Purkait, D. Dey, and S. Chakravorti, “Monitoring of inter-turn insulation failure in induction motor using advanced signal and data processing tools,” IEEE Trans. Dielectr. Electr. Insul., vol. 18, no. 5, pp. 746–751, 2002. [18] S. Choi, B. Akin, M. M. Rahimian, and H. A. Toliyat, “Performance-Oriented Electric Motors Diagnostics in Modern Energy Conversion Systems,” IEEE Transactions on Industrial Electronics, vol. 59, no. 2. pp. 1266–1277, 2012. [19] S. Choi, E. Pazouki, J. Baek, and H. R. Bahrami, “Iterative Condition Monitoring and Fault Diagnosis Scheme of Electric Motor for Harsh Industrial Application,” IEEE Transactions on Industrial Electronics, vol. 62, no. 3. pp. 1760–1769, 2015. [20] S. Cheng, S. Member, P. Zhang, and T. G. Habetler, “An Impedance Identification Approach to Sensitive Detection and Location of Stator Turn-to-Turn Faults in a Closed-Loop Multiple-Motor Drive,” IEEE Trans. Ind. Electron., vol. 58, no. 5, pp. 1545–1554, 2011. [21] Kyusung Kim, A. G. Parlos, and R. M. Bharadwaj, “Sensorless fault diagnosis of induction motors,” IEEE Trans. Ind. Electron., vol. 50, no. 5, pp. 1038–1051, Oct. 2003. [22] S. H. Kia, H. Henao, and G. A. Capolino, “A comparative study of acoustic, vibration and stator current signatures for gear tooth fault diagnosis,” Proc. - 2012 20th Int. Conf. Electr. Mach. ICEM 2012, no. 1, pp. 1514–1519, 2012. [23] G. A. Capolino, J. A. Antonino-Daviu, and M. Riera-Guasp, “Modern diagnostics techniques for electrical machines, power electronics, and drives,” IEEE Trans. Ind. Electron., vol. 62, no. 3, p. 8, 2015. [24] E. T. Esfahani, S. Wang, and V. Sundararajan, “Multisensor wireless system for eccentricity and bearing fault detection in induction motors,” IEEE/ASME Trans. Mechatronics, vol. 19, no. 3, pp. 818–826, Jun. 2014. [25] A. Schmitt, Helder Luiz, Silva L.R.B, Scalassara, P.R.,Goedtel, “Bearing Fault Detection Using Relative Entropy of Wavelet Components And Artificial Neural Networks,” pp. 538–543, 2013. [26] J. Pons-Llinares, J. A. Antonino-Daviu, M. Riera-Guasp, S. Bin Lee, T. J. Kang, and C. Yang, “Advanced induction motor rotor fault diagnosis via continuous and discrete time-frequency tools,” IEEE Trans. Ind. Electron., vol. 62, no. 3, pp. 1791–1802, 2015. [27] G. Jagadanand and F. L. Dias, “ARM based induction motor fault detection using wavelet and support vector machine,” Signal Processing, Informatics, Communication and Energy Systems (SPICES), 2015 IEEE International Conference on. pp. 1–4, 2015. [28] D. Zurita, M. Delgado, J. A. Ortega, and L. Romeral, “Intelligent sensor based on acoustic emission analysis applied to gear fault diagnosis,” Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED), 2013 9th IEEE International Symposium on. pp. 169–176, 2013. [29] M. Delgado Prieto, D. Zurita Millan, W. Wang, A. Machado Ortiz, J. A. Ortega Redondo, and L. Romeral Martinez, “Self-powered wireless sensor applied to gear diagnosis based on acoustic emission,” IEEE Trans. Instrum. Meas., vol. 65, no. 1, pp. 15–24, 2016. [30] O. Yaman, I. Aydin, M. Karaköse, and E. Akinn, “Wireless sensor network based fault diagnosis approaches,” Signal Processing and Communications Applications Conference (SIU), 2013 21st. pp. 1–4, 2013. [31] P. S. Barendse, B. Herndler, M. A. Khan, and P. Pillay, “The application of wavelets for the detection of inter-turn faults in induction machines,” 2009 IEEE Int. Electr. Mach. Drives Conf. IEMDC ’09, pp. 1401–1407, 2009. [32] N. R. Devi, S. A. Gafoor, and P. V. R. Rao, “Wavelet ANN based stator internal faults protection scheme for 3-phase induction motor,” Proc. 2010 5th IEEE Conf. Ind. Electron. Appl. ICIEA 2010, pp. 1457–1461, 2010. [33] N. Rama Devi, D. V. S. S. Siva Sarma, and P. V. Ramana Rao, “Detection of stator incipient faults and identification of faulty phase in three-phase induction motor – simulation and experimental verification,” IET Electr. Power Appl., vol. 9, no. 8, pp. 540–548, 2015. [34] M. A. S. K. Khan, T. S. Radwan, and M. A. Rahman, “Real-time implementation of wavelet packet transform-based diagnosis and protection of three-phase induction motors,” IEEE Trans. Energy Convers., vol. 22, no. 3, pp. 647–655, 2007. [35] J. A. Rosero, L. Romeral, J. A. Ortega, and E. Rosero, “Short-circuit detection by means of empirical mode decomposition and Wigner-Ville distribution for PMSM running under dynamic condition,” IEEE Trans. Ind. Electron., vol. 56, no. 11, pp. 4534–4547, 2009. [36] E. Elbouchikhi, V. Choqueuse, Y. Amirat, M. Benbouzid, and S. Turri, “An Efficient Hilbert-Huang Transform-based Bearing Faults Detection in Induction Machines,” IEEE Trans. Energy Convers., vol. 32, no. 2, pp. 1–1, 2017. [37] E. Elbouchikhi, V. Choqueuse, Y. Trachi, and M. Benbouzid, “Induction machine bearing faults detection based on Hilbert-Huang transform,” IEEE Int. Symp. Ind. Electron., vol. 2015–Septe, pp. 843–848, 2015. [38] M. Amarnath and I. R. Praveen Krishna, “Empirical mode decomposition of acoustic signals for diagnosis of faults in gears and rolling element bearings,” IET Sci. Meas. Technol., vol. 6, no. 4, p. 279, 2012. [39] G.-J. Feng, J. Gu, D. Zhen, M. Aliwan, F.-S. Gu, and A. D. Ball, “Implementation of envelope analysis on a wireless condition monitoring system for bearing fault diagnosis,” Int. J. Autom. Comput., vol. 12, no. 1, pp. 14–24, Feb. 2015. [40] V. C. M. N. Leite, J. G. Borges Da Silva, G. F. C. Veloso, L. E. Borges Da Silva, G. Lambert-Torres, E. L. Bonaldi, and L. E. De Lacerda De Oliveira, “Detection of localized bearing faults in induction machines by spectral kurtosis and envelope analysis of stator current,” IEEE Trans. Ind. Electron., vol. 62, no. 3, pp. 1855–1865, 2015. [41] D. He, R. Li, and J. Zhu, “Plastic Bearing Fault Diagnosis Based on a Two-Step Data Mining Approach,” IEEE Trans. Ind. Electron., vol. 60, no. 8, pp. 1–1, 2012. [42] S. Mallat, “Wavelet Bases,” in A Wavelet Tour of Signal Processing, Third., Elsevier Ltd, 2009, pp. 263–376. [43] H. Douglas, P. Pillay, and P. Barendse, “The detection of interturn stator faults in doubly-fed induction generators,” in Conference Record - IAS Annual Meeting (IEEE Industry Applications Society), 2005, vol. 2, pp. 1097–1102. [44] N. Laouti, S. Othman, M. Alamir, and N. Sheibat-Othman, “Combination of Model-based Observer and Support Vector Machines for Fault Detection of Wind Turbines,” Int. J. Autom. Comput., vol. 11, no. 3, pp. 274–287, Mar. 2015. [45] D. A. Asfani, M. H. Purnomo, and D. R. Sawitri, “Naïve Bayes classifier for Temporary short circuit fault detection in Stator Winding,” pp. 288–294, 2013. [46] H. O. Vishwakarma, K. S. Sajan, B. Maheshwari, and Y. D. Dhiman, “Intelligent bearing fault monitoring system using support vector machine and wavelet packet decomposition for induction motors,” Power and Advanced Control Engineering (ICPACE), 2015 International Conference on. pp. 339–343, 2015. [47] V. Vetiska, O. Hyncica, C. Ondrusek, and Z. Hadas, “Autonomous monitoring unit of fault condition with vibration energy harvester,” 2015 IEEE 15th Int. Conf. Environ. Electr. Eng. EEEIC 2015 - Conf. Proc., pp. 980–985, 2015. [48] J. Neuzil, O. Kreibich, and R. Smid, “A distributed fault detection system based on IWSN for machine condition monitoring,” IEEE Trans. Ind. Informatics, vol. 10, no. 2, pp. 1118–1123, 2014. [49] W. Ikram, S. L. Chen, T. Harvei, T. Olsen, E. Mikalsen, G. Svoen, S. Froystein, and B. Myhre, “Vibration-based wireless machine condition monitoring system,” Emerging Technology and Factory Automation (ETFA), 2014 IEEE. pp. 1–4, 2014. [50] F. Philipp, J. Martinez, M. Glesner, and A. Arkkio, “A smart wireless sensor for the diagnosis of broken bars in induction motors,” Proc. Bienn. Balt. Electron. Conf. BEC, no. 1, pp. 119–122, 2012. [51] University of California - Berkeley and Stanford University, “TinyOS Documentation Wiki.” [Online]. Available: http://tinyos.stanford.edu/tinyos-wiki/index.php/Main_Page. [Accessed: 18-Aug-2017]. [52] O. Kreibich, J. Neuzil, and R. Smid, “Quality-based multiple-sensor fusion in an industrial wireless sensor network for MCM,” IEEE Trans. Ind. Electron., vol. 61, no. 9, pp. 4903–4911, 2014. [53] G. M. Joksimovic and J. Penman, “The detection of inter-turn short circuits in the stator windings of operating motors,” IEEE Trans. Ind. Electron., vol. 47, no. 5, pp. 1078–1084, 2000. [54] M. A. Delgado Narváez, “Monitoreo y Diagnóstico de Electric Machine Drive Systems (EMDS),” Universidad Nacional de Colombia - Sede Bogotá, 2017. [55] G. G. Yen and K.-C. Lin, “Wavelet packet feature extraction for vibration monitoring,” IEEE Trans. Ind. Electron., vol. 47, no. 3, pp. 650–667, 2000. [56] I. Daubechies and W. Sweldens, “Factoring wavelet transforms into lifting steps,” J. Fourier Anal. Appl., vol. 4, no. 3, pp. 247–269, 1998. [57] F. L. I. A. Dias and G. Jagadanand, “ARM based wavelet transform implementation for embedded system applications,” pp. 122–126, 2014. [58] J. Antoni, “The spectral kurtosis: A useful tool for characterising non-stationary signals,” Mech. Syst. Signal Process., vol. 20, no. 2, pp. 282–307, 2006. [59] P. H. Nguyen and J. M. Kim, “Multifault diagnosis of rolling element bearings using a wavelet kurtogram and vector median-based feature analysis,” Shock Vib., vol. 2015, no. October 2016, 2015. [60] J. Antoni, “Fast computation of the kurtogram for the detection of transient faults,” Mech. Syst. Signal Process., vol. 21, no. 1, pp. 108–124, 2007. [61] G. A., J. Manuel, J.-L. Marty, and R. Munoz, “Implementation of the Discrete Wavelet Transform Used in the Calibration of the Enzymatic Biosensors,” Discret. Wavelet Transform. - Biomed. Appl., 2012. [62] G. R. Lee, R. Gommers, F. Waselewski, K. Wohlfahrt, and A. O’Leary, “PyWavelets: A Python package for wavelet analysis,” J. Open Source Softw., vol. 4, no. 36, p. 1237, 2019. [63] J. Vanderplas, “In-Depth: Support Vector Machines,” in Python Data Science Handbook, O’Reilly Media, 2016, p. 541. [64] L. Buitinck, G. Louppe, M. Blondel, F. Pedregosa, A. Mueller, O. Grisel, V. Niculae, P. Prettenhofer, A. Gramfort, J. Grobler, R. Layton, J. Vanderplas, A. Joly, B. Holt, and G. Varoquaux, “API design for machine learning software: experiences from the scikit-learn project,” ECML PKDD Workshop: Languages for Data Mining and Machine Learning, pp. 108–122, 2013. [65] G. Feng, A. Mustafa, J. X. Gu, D. Zhen, F. Gu, and A. D. Ball, “The real-time implementation of envelope analysis for bearing fault diagnosis based on wireless sensor network,” Automation and Computing (ICAC), 2013 19th International Conference on. pp. 1–6, 2013. [66] Maxim Integrated, “MAX291/MAX292/MAX295/MAX296 8th-Order, Lowpass, Switched-Capacitor Filters,” no. Rev 5., pp. 1–10. [67] D. Navarro, “SystemC Models : Blocks - Documentation,” Lyon Institute of Nanotechnologies - France. [Online]. Available: http://idea1inl.free.fr/IDEA1/links.html. [68] W. Du, F. Mieyeville, D. Navarro, and I. O. Connor, “IDEA1: A validated SystemC-based system-level design and simulation environment for wireless sensor networks,” Eurasip J. Wirel. Commun. Netw., vol. 2011, no. 1, pp. 1–20, 2011. [69] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, 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,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011. [70] J. A. Caballero Peña and J. A. Rosero, “Procedimiento: Medición de desempeño de motores bajo carga (motor + driver) con variador de velocidad regenerativo,” Bogotá D.C., Rev_A, 2018.ORIGINAL1032449819.2019.pdf1032449819.2019.pdfapplication/pdf5796042https://repositorio.unal.edu.co/bitstream/unal/75656/1/1032449819.2019.pdf240bcb593ce28b3e2f8eee793046b74fMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-83991https://repositorio.unal.edu.co/bitstream/unal/75656/2/license.txt6f3f13b02594d02ad110b3ad534cd5dfMD52CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.unal.edu.co/bitstream/unal/75656/3/license_rdf4460e5956bc1d1639be9ae6146a50347MD53THUMBNAIL1032449819.2019.pdf.jpg1032449819.2019.pdf.jpgGenerated Thumbnailimage/jpeg5488https://repositorio.unal.edu.co/bitstream/unal/75656/4/1032449819.2019.pdf.jpg010c555be548aa3a6acbf26f145a90a0MD54unal/75656oai:repositorio.unal.edu.co:unal/756562024-04-08 23:13:40.794Repositorio Institucional Universidad Nacional de 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W5pbyBww7pibGljby4gU2kgbm8gZnVlc2UgZWwgY2FzbywgYWNlcHRvIHRvZGEgbGEgcmVzcG9uc2FiaWxpZGFkIHBvciBjdWFscXVpZXIgaW5mcmFjY2nDs24gZGUgZGVyZWNob3MgZGUgYXV0b3IgcXVlIGNvbmxsZXZlIGxhIGRpc3RyaWJ1Y2nDs24gZGUgZXN0b3MgYXJjaGl2b3MgeSBtZXRhZGF0b3MuCkFsIGhhY2VyIGNsaWMgZW4gZWwgc2lndWllbnRlIGJvdMOzbiwgdXN0ZWQgaW5kaWNhIHF1ZSBlc3TDoSBkZSBhY3VlcmRvIGNvbiBlc3RvcyB0w6lybWlub3MuCg==

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