Publicado:

2020-11-17

Número:

Vol. 14 Núm. 2 (2020)

Sección:

Visión de Caso

Distributed Fault Diagnosis System based on Wireless Sensor Networks

Sistema de diagnóstico distribuido de fallas basado en redes inalámbricas de sensores

Autores/as

Palabras clave:

Análisis distribuido, Diagnóstico de fallas, Motor de Inducción, Motor-Current Signature Analysis, Corriente de estator, Redes inalámbricas de sensores, ZigBee (es).

Palabras clave:

Distributed Analysis, Fault Diagnosis, Induction Motor, Motor-Current Signature Analysis, Stator Current, Wireless Sensor Networks, ZigBee (en).

Resumen (en)

This article presents the development of a distributed fault diagnosis and monitoring system whose remote nodes are responsible for data collection and distributed analysis to identify problems that could lead to critical faults in industrial processes or systems. The developed intelligent remote node was implemented with MCU LPCXpresso54114 connected to a ZigBee protocol wireless sensor network through XBee communication module. The gateway node is a Raspberrry PI with HTTP communication and JSON format to the PI System industrial monitoring system database. Motor Current Signature Analysis (MCSA) was implemented and validated to identify interturn faults of induction motors. The developed platform is a tool to perform comparison and validation of analysis techniques, indicators, and fault classification, because there are different combinations that can be applied to improve diagnosis reliability, fault observability, differentiation between fault conditions, classification accuracy, tolerance to transients, sensitivity, among others.

Resumen (es)

En este artículo presenta el desarrollo de un sistema de monitoreo y diagnóstico distribuido 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 dispositivo desarrollado como 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 está compuesto de una tarjeta Raspberrry PI con comunicación mediante protocolo HTTP y formato JSON a la base de datos del sistema de monitoreo industrial PI System. Se implementó y validó el acondicionamiento de señal para la medición de corrientes de estator (MCSA) que permitió identificar fallas entre espiras de motores de inducción tipo jaula de ardilla. La plataforma presentada finalmente es una herramienta para realizar comparación y validación de técnicas de análisis, indicadores y de clasificación de fallas, puesto que existen diversas combinaciones que pueden ser aplicadas con el fin de mejorar la confiabilidad del diagnóstico, la observación de la falla, la diferenciación entre condiciones de falla, la precisión de la clasificación, la tolerancia a transitorios, sensibilidad, entre otros.

Biografía del autor/a

Javier Alveiro Rosero García, Universidad Nacional de Colombia

Ingeniero eléctrico

Universidad Del Valle

Doctorado en Ingeniera Electrónica

Universidad Politécnica De Cataluña

Referencias

J. P. Amaro, F. J. T. E. Ferreira, R. Cortesão, N. Vinagre, 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. https://doi.org/10.1109/ICIT.2010.5472560

M. Bordasch, C. Brand, P. Gohner, "Fault-based identification and inspection of fault developments to enhance availability in industrial automation systems", IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA), 2015, pp. 1-8. https://doi.org/10.1109/ETFA.2015.7301515

L. Hou, 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. https://doi.org/10.1109/TIM.2012.2200817

F. J. Ferreira, G. Baoming, A. T. de Almeida, "Reliability and operation of high-efficiency induction motors", IEEE/IAS 51st Ind. Commer. Power Syst. Tech. Conf., pp. 1-13, 2015. https://doi.org/10.1109/ICPS.2015.7266412

A. Gandhi, T. Corrigan, 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. https://doi.org/10.1109/TIE.2010.2089937

N. W. Bergmann, L. Q. Hou, "Energy Efficient Machine Condition Monitoring Using Wireless Sensor Networks", Int. Conf. Wirel. Commun. Sens. Netw., pp. 285-290, 2014. https://doi.org/10.1109/WCSN.2014.65

L. Hou, N. W. Bergmann, "Induction motor condition monitoring using industrial wireless sensor networks", Sixth Int. Conf. Intell. Sensors, Sens. Networks Inf. Process., pp. 49-54, 2010. https://doi.org/10.1109/ISSNIP.2010.5706739

S. Nandi, H. A. Toliyat, X. Li, "Condition monitoring and fault diagnosis of electrical motors - A review", IEEE Trans. Energy Convers., vol. 20, no. 4, pp. 719-729, 2005. https://doi.org/10.1109/TEC.2005.847955

R. Windings, "In-service monitoring of stator and rotor windings," pp. 389-437, 2014. https://doi.org/10.1002/9781118886663.ch16

G. Jagadanand, F. L. Dias, "ARM based induction motor fault detection using wavelet and support vector machine", IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES), pp. 1-4, 2015. https://doi.org/10.1109/SPICES.2015.7091503

M. A. Khan, T. S. Radwan, 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. https://doi.org/10.1109/TEC.2006.882417

S. Mallat, "Wavelet Bases," in A Wavelet Tour of Signal Processing, Third., Elsevier Ltd, pp. 263-376, 2009. https://doi.org/10.1016/B978-0-12-374370-1.00011-2

H. Douglas, P. Pillay, P. Barendse, "The detection of interturn stator faults in doubly-fed induction generators", Conference Record - IAS Annual Meeting (IEEE Industry Applications Society), vol. 2, pp. 1097-1102, 2005.

A. Sapena-Baño, "Condition monitoring of electrical machines using low computing power devices", Int. Conf. Electr. Mach., pp. 1516-1522, 2014. https://doi.org/10.1109/ICELMACH.2014.6960383

S. Das, P. Purkait, D. Dey, 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. https://doi.org/10.1109/TDEI.2011.6032830

P. S. Barendse, B. Herndler, M. A. Khan, P. Pillay, "The application of wavelets for the detection of inter-turn faults in induction machines", IEEE Int. Electr. Mach. Drives Conf. IEMDC '09, pp. 1401-1407, 2009. https://doi.org/10.1109/IEMDC.2009.5075386

N. R. Devi, S. A. Gafoor, 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.

N. Rama Devi, D. V. Siva Sarma, 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. https://doi.org/10.1049/iet-epa.2015.0024

N. Laouti, S. Othman, M. Alamir, N. Sheibat, "Combination of Model-based Observer and Support Vector Machines for Fault Detection of Wind Turbines", Int. J. Autom. Comp., vol. 11, no. 3, pp. 274-287, 2015. https://doi.org/10.1007/s11633-014-0790-9

D. A. Asfani, M. H. Purnomo, D. R. Sawitri, "Naïve Bayes classifier for Temporary short circuit fault detection in Stator Winding", 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED), pp. 288-294, 2013. https://doi.org/10.1109/DEMPED.2013.6645730

S. Choi, B. Akin, M. M. Rahimian, 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. https://doi.org/10.1109/TIE.2011.2158037

G. A. Capolino, J. A. Antonino-Daviu, M. Riera-Guasp, "Modern diagnostics techniques for electrical machines, power electronics, and drives", IEEE Trans. Ind. Electron., vol. 62, no. 3, p. 8, 2015. https://doi.org/10.1109/TIE.2015.2391186

H. Henao, "Trends in Fault Diagnosis for Electrical Machines: A Review of Diagnostic Techniques", IEEE Ind. Electron. Mag., vol. 8, no. 2, pp. 31-42, 2014. https://doi.org/10.1109/MIE.2013.2287651

A. Schmitt, H. Silva, R. Scalassara, P. Goedtel, "Bearing Fault Detection Using Relative Entropy of Wavelet Components and Artificial Neural Networks", pp. 538-543, 2013. https://doi.org/10.1109/DEMPED.2013.6645767

L. Hou, N. W. Bergmann, "Induction motor fault diagnosis using industrial wireless sensor networks and Dempster-Shafer classifier fusion", IECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society, pp. 2992-2997, 2011. https://doi.org/10.1109/IECON.2011.6119786

G. Feng, A. Mustafa, J. X. Gu, D. Zhen, F. Gu, A. D. Ball, "The real-time implementation of envelope analysis for bearing fault diagnosis based on wireless sensor network", 19th International Conference on Automation and Computing (ICAC), pp. 1-6, 2013.

E. T. Esfahani, S. Wang, 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, 2014. https://doi.org/10.1109/TMECH.2013.2260865

Maxim Integrated, "MAX291/MAX292/MAX295/MAX296 8th-Order, Lowpass, Switched-Capacitor Filters", no. Rev 5., pp. 1-10.

L. Buitinck, "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.

F. Pedregosa, "Scikit-learn: Machine Learning in Python", J. Mach. Learn. Res., vol. 12, pp. 2825-2830, 2011.

G. M. Joksimovic, 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. https://doi.org/10.1109/41.873216

M. A. Delgado Narváez, "Monitoreo y Diagnóstico de Electric Machine Drive Systems (EMDS)", Universidad Nacional de Colombia, 2017.

Cómo citar

APA

Rosero García, J. A., & Caballero Peña, J. A. (2020). Distributed Fault Diagnosis System based on Wireless Sensor Networks. Visión electrónica, 14(2). Recuperado a partir de https://revistas.udistrital.edu.co/index.php/visele/article/view/17058

ACM

[1]
Rosero García, J.A. y Caballero Peña, J.A. 2020. Distributed Fault Diagnosis System based on Wireless Sensor Networks. Visión electrónica. 14, 2 (nov. 2020).

ACS

(1)
Rosero García, J. A.; Caballero Peña, J. A. Distributed Fault Diagnosis System based on Wireless Sensor Networks. Vis. Electron. 2020, 14.

ABNT

ROSERO GARCÍA, J. A.; CABALLERO PEÑA, J. A. Distributed Fault Diagnosis System based on Wireless Sensor Networks. Visión electrónica, [S. l.], v. 14, n. 2, 2020. Disponível em: https://revistas.udistrital.edu.co/index.php/visele/article/view/17058. Acesso em: 24 jul. 2021.

Chicago

Rosero García, Javier Alveiro, y Jairo Andrés Caballero Peña. 2020. «Distributed Fault Diagnosis System based on Wireless Sensor Networks». Visión electrónica 14 (2). https://revistas.udistrital.edu.co/index.php/visele/article/view/17058.

Harvard

Rosero García, J. A. y Caballero Peña, J. A. (2020) «Distributed Fault Diagnosis System based on Wireless Sensor Networks», Visión electrónica, 14(2). Disponible en: https://revistas.udistrital.edu.co/index.php/visele/article/view/17058 (Accedido: 24julio2021).

IEEE

[1]
J. A. Rosero García y J. A. Caballero Peña, «Distributed Fault Diagnosis System based on Wireless Sensor Networks», Vis. Electron., vol. 14, n.º 2, nov. 2020.

MLA

Rosero García, J. A., y J. A. Caballero Peña. «Distributed Fault Diagnosis System based on Wireless Sensor Networks». Visión electrónica, vol. 14, n.º 2, noviembre de 2020, https://revistas.udistrital.edu.co/index.php/visele/article/view/17058.

Turabian

Rosero García, Javier Alveiro, y Jairo Andrés Caballero Peña. «Distributed Fault Diagnosis System based on Wireless Sensor Networks». Visión electrónica 14, no. 2 (noviembre 17, 2020). Accedido julio 24, 2021. https://revistas.udistrital.edu.co/index.php/visele/article/view/17058.

Vancouver

1.
Rosero García JA, Caballero Peña JA. Distributed Fault Diagnosis System based on Wireless Sensor Networks. Vis. Electron. [Internet]. 17 de noviembre de 2020 [citado 24 de julio de 2021];14(2). Disponible en: https://revistas.udistrital.edu.co/index.php/visele/article/view/17058

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