Correo Electrónico
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Diagnóstico descentralizada de fallas entre espiras de motores de inducción basado en redes inalámbricas de sensores
Decentralized inter-turn fault diagnosis of induction motors based on wireless sensor networks
DOI:
https://doi.org/10.15446/dyna.v88n216.88851Palabras clave:
Análisis descentralizado, Corriente de estator, Diagnóstico de fallas, Falla entre Espiras, Motor-Current Signature Analysis, Motor de Inducción, Redes inalámbricas de sensores (es)decentralized analysis;, fault diagnosis;, induction motor;, inter-turn fault;, motor-current signature analysis;, stator current;, wireless sensor networks (en)
El diagnóstico de fallas de motores ha alcanzado múltiples avances e integrado diversas técnicas de análisis y clasificación de datos proporcionando ventajas como tolerancia al ruido, a cambios en el punto de operación o transitorios; pero requieren ser analizadas para lograr su integración en dispositivos programables e identificar sus mejoras; por ello, se desarrolló e implementó un sistema de diagnóstico descentralizado de fallas de motores de inducción basado en redes inalámbricas de sensores - WSN con una clasificación de datos basado en Support Vector Machine – SVM. En este trabajo se plantearon indicadores basados en la medición de las corrientes de estator (Motor-Current Signature Analysis - MCSA), Fast Fourier Transform - FFT y Discrete Wavelet Transform - DWT con los cuales se logró validar el sistema e identificar un comportamiento diferenciado de falla entre espiras como un antecedente de fallas críticas al presentarse como un deterioro del aislamiento.
Motor’s fault diagnosis has achieved multiples advances and has integrated different analysis and data classification techniques with the purpose of giving noise tolerance, electric transients tolerance and withstand changes of operating point; but these must be analyzed to achieve their integration in programmable devices and to identify their improvements. Therefore, a decentralized induction motor fault monitoring and diagnosis was developed and implemented, this was based on Wireless Sensor Networks – WSN and Support Vector Machine – SVM as data classifier. In this paper, Indicators were established based on Motor-Current Signature Analysis - MCSA, Fast Fourier Transform - FFT and Discrete Wavelet Transform DWT with which it is possible to validate and identify a differentiated behavior of incipient interturn fault of critical faults like phase-phase and phase-neutral short circuits when isolation deterioration is presented
Referencias
Amaro J.P., Ferreira F.J.T.E., Cortesão R., Vinagre N., and Bras R.P., Low cost wireless sensor network for in-field operation monitoring of induction motors. In: Proc. IEEE Int. Conf. Ind. Technol., pp. 1044-1049, 2010. DOI: 10.1109/ICIT.2010.5472560
Bordasch, M., Brand, C. and Gohner, P., 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, pp. 1-8. DOI: 10.1109/ETFA.2015.7301515
Hou, L. and Bergmann, N.W., Novel industrial wireless sensor networks for machine condition monitoring and fault diagnosis. IEEE Trans. Instrum. Meas., 61(10), pp. 2787-2798, 2012. DOI: 10.1109/TIM.2012.2200817
Nandi, S., Toliyat, H.A. and Li, X., Condition monitoring and fault diagnosis of electrical motors - A review. IEEE Trans. Energy Convers., 20(4), pp. 719-729, 2005. DOI: 10.1109/TEC.2005.847955
Henao, H., Capolino, G.-A., Fernandez-Cabanas, M., Filippetti, F., Bruzzese, C., Strangas, E., Pusca, R., Estima, J., Riera-Guasp, M. and Hedayati-Kia, S., Trends in fault diagnosis for electrical machines: a review of diagnostic techniques. IEEE Ind. Electron. Mag., 8(2), pp. 31-42, 2014. DOI: 10.1109/MIE.2013.2287651
Sapena-Baño, A., Perez-Cruz, J., Pineda-Sanchez, M., Puche-Panadero, R., Roger-Folch, J., Riera-Guasp, M. and Martinez-Roman, J., Condition monitoring of electrical machines using low computing power devices. In: 2014 Int. Conf. Electr. Mach., 2014, pp. 1516-1522. DOI: 10.1109/ICELMACH.2014.6960383
Riera-Guasp, M., Antonino-Daviu, J.A., and Capolino, G.-A., Advances in electrical machine, power electronic, and drive condition monitoring and fault detection: state of the art. IEEE Trans. Ind. Electron., 62(3), pp. 1746-1759, 2015. DOI: 10.1109/TIE.2014.2375853
Kia, S.H., Henao, H., Member, S. and Capolino, G., Efficient digital signal processing techniques for induction machines fault diagnosis. Electr. Mach. Des. Control Diagnosis (WEMDCD), in: 2013 IEEE Work., 2013, pp. 232-246. DOI: 10.1109/WEMDCD.2013.6525183
Gandhi, A., Corrigan, T. and Parsa, L., Recent advances in modeling and online detection of stator interturn faults in electrical motors. IEEE Transactions on Industrial Electronics, 58(5). pp. 1564-1575, 2011. DOI: 10.1109/TIE.2010.2089937
Das, S., Purkait, P., Dey, D. and Chakravorti, S., Monitoring of inter-turn insulation failure in induction motor using advanced signal and data processing tools. IEEE Trans. Dielectr. Electr. Insul., 18(5), pp. 746-751, 2002. DOI: 10.1109/TDEI.2011.6032830
Rosero-García J.A. and Caballero-Peña J.A., Distributed Fault diagnosis system base don wireless sensor Networks. Vision Electrónica, [online]. 14(2), pp. 1-27, 2020. Available at: https://revistas.udistrital.edu.co/index.php/visele/article/view/17058
Jagadanand, G. and Dias, F.L., ARM based induction motor fault detection using wavelet and support vector machine. Signal Processing, Informatics, Communication and Energy Systems (SPICES), in: 2015 IEEE International Conference on. 2015, pp. 1-4. DOI: 10.1109/SPICES.2015.7091503
Choi, S., Pazouki, E., Baek, J. and Bahrami, H.R., Iterative Condition monitoring and fault diagnosis scheme of electric motor for harsh industrial application. IEEE Transactions on Industrial Electronics, 62(3), pp. 1760-1769, 2015. DOI: 10.1109/TIE.2014.2361112
Zurita, D., Delgado, M., Ortega, J.A. and Romeral, L., Intelligent sensor based on acoustic emission analysis applied to gear fault diagnosis. Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED), in: 2013 9th IEEE International Symposium on. 2013, pp. 169-176. DOI: 10.1109/DEMPED.2013.6645713
Delgado-Prieto, M., Zurita-Millan, D., Wang, W., Machado-Ortiz, A., Ortega-Redondo, J.A. and Romeral-Martinez, L., Self-powered wireless sensor applied to gear diagnosis based on acoustic emission. IEEE Trans. Instrum. Meas., 65(1), pp. 15-24, 2016. DOI: 10.1109/TIM.2015.2476278
Yaman, O., Aydin, I., Karaköse, M. and Akinn, E., Wireless sensor network based fault diagnosis approaches. In: 21st Signal Processing and Communications Applications Conference (SIU), 2013, pp. 1-4, 2013. DOI: 10.1109/SIU.2013.6531587
Sahoo, S.K., Rodriguez, P., and Savinovic, D., Experimental investigation of different fault indicators for Synchronous machine failure analysis. Proc. - 2015 IEEE Int. Electr. Mach. Drives Conf. IEMDC 2015(May), pp. 1398-1404, 2016. DOI: 10.1109/IEMDC.2015.7409245
Rama-Devi, N., Siva-Sarma, D.V S.S. and Ramana-Rao P.V., Detection of stator incipient faults and identification of faulty phase in three-phase induction motor - simulation and experimental verification. IET Electr. Power Appl., 9(8), pp. 540-548, 2015. DOI: 10.1049/iet-epa.2015.0024
Khan, M.A.S.K., Radwan, T.S. and Rahman, M.A., Real-time implementation of wavelet packet transform-based diagnosis and protection of three-phase induction motors. IEEE Trans. Energy Convers., 22(3), pp. 647-655, 2007. DOI: 10.1109/TEC.2006.882417
Lashkari, N. and Poshtan, J., Detection and discrimination of stator interturn fault and unbalanced supply voltage fault in induction motor using neural network. Power Electronics, Drives Systems & Technologies Conference (PEDSTC), 2015 6th. pp. 275-280, 2015. DOI: 10.1109/PEDSTC.2015.7093287
Caballero-Peña, J.A., Sistema de diagnóstico distribuido de motores de inducción basado en redes inalámbricas de sensores y protocolo ZigBee. MSc. Thesis, Universidad Nacional de Colombia - Sede Bogotá, 2019.
Hou, L. and Bergmann, N.W., 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. DOI: 10.1109/IECON.2011.6119786
Bergmann, N.W. and Hou, L.-Q., Energy efficient machine condition monitoring using wireless sensor Networks. In: 2014 Int. Conf. Wirel. Commun. Sens. Netw., 2014, pp. 285-290. DOI: 10.1109/WCSN.2014.65
Kreibich, O., Neuzil, J, and Smid, R., Quality-based multiple-sensor fusion in an industrial wireless sensor network for MCM. IEEE Trans. Ind. Electron., 61(9), pp. 4903-4911, 2014. DOI: 10.1109/TIE.2013.2293710
Delgado-Narváez, M.A., Monitoreo y Diagnóstico de Electric Machine Drive Systems (EMDS), M.c. Thesis, Universidad Nacional de Colombia - Sede Bogotá, 2017.
Joksimovic, G.M. and Penman, J., The detection of inter-turn short circuits in the stator windings of operating motors. IEEE Trans. Ind. Electron., 47(5), pp. 1078-1084, 2000. DOI: 10.1109/41.873216
Mallat, S., Wavelet Bases. In: A Wavelet Tour of Signal Processing, 3rd, Elsevier Ltd, 2009, pp. 263-376. DOI: 10.1016/B978-0-12-374370-1.00011-2
Yen, G.G. and Lin, K.-C., Wavelet packet feature extraction for vibration monitoring. IEEE Trans. Ind. Electron., 47(3), pp. 650-667, 2000. DOI: 10.1109/CCA.1999.801206
Daubechies, I. and Sweldens, W., Factoring wavelet transforms into lifting steps. J. Fourier Anal. Appl., 4(3), pp. 247-269, 1998. DOI: 10.1007/BF02476026
Leite, V.C.M.N., Borges-Da Silva, J.G., Veloso, G.F.C., Borges-Da Silva, L.E., Lambert-Torres, G., Bonaldi, E.L. and De Lacerda-De Oliveira, L.E., Detection of localized bearing faults in induction machines by spectral kurtosis and envelope analysis of stator current. IEEE Trans. Ind. Electron., 62(3), pp. 1855-1865, 2015. DOI: 10.1109/TIE.2014.2345330
Antoni, J., The spectral kurtosis: a useful tool for characterising non-stationary signals. Mech. Syst. Signal Process., 20(2), pp. 282-307, 2006. DOI: 10.1016/j.ymssp.2004.09.001
Nguyen, P.H. and Kim, J.M., Multifault diagnosis of rolling element bearings using a wavelet kurtogram and vector median-based feature analysis. Shock Vib., 2015(October), 2016. DOI: 10.1155/2015/320508
Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., Niculae, V., Prettenhofer, P., Gramfort, A., Grobler, J., Layton, R., Vanderplas, J., Joly, A., Holt, B., and Varoquaux, G., API design for machine learning software: experiences from the scikit-learn project. In: ECML PKDD Workshop: Languages for Data Mining and Machine Learning, [online]. 2013, pp. 108-122. Available at: https://arxiv.org/abs/1309.0238v1
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E., Scikit-learn: machine learning Python. J. Mach. Learn. Res., 12, pp. 2825-2830, 2011.
Vanderplas, J., In-Depth: support vector machines. in Python Data Science Handbook, O’Reilly Media, [online]. 2016, 541 P. [Consulted 8-02-21] Available at: https://jakevdp.github.io/PythonDataScience Handbook/
Alonso, G.A., Gutiérrez, J.M., Marty, J.-L. and Munoz, R., Implementation of the Discrete Wavelet Transform Used in the Calibration of the Enzymatic Biosensors. Discret. Wavelet Transform. In: Olkkonen, H., Ed., Discrete Wavelet Transforms - Biomedical Applications, 2012. DOI. 10.5775/20957
Lee, G.R., Gommers, R., Waselewski, F., Wohlfahrt, K. and O’Leary, A., PyWavelets: a Python package for wavelet analysis. J. Open Source Softw., 4(36), art. 1237, 2019. DOI: 10.21105/joss.01237
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