REVISIÓN DE LOS MÉTODOS DE DETECCIÓN DE FALLAS EN MOTORES SÍNCRONOS DE IMANES PERMANENTES CON APLICACIONES PARA INDUSTRIA 4.0 (REVIEW OF FAULT DETECTION METHODS FOR PERMANENT MAGNET SYNCHRONOUS MACHINES WITH APPLICATIONS FOR INDUSTRY 4.0)

Rafael Torres Medina, Víctor Arturo Maldonado Ruelas, Raúl Arturo Ortiz Medina

Resumen


Resumen

En este trabajo se presenta el estado del arte de la investigación existente en la metodología para el diagnóstico de fallas en motores síncronos de imanes permanentes (PMSM, por sus siglas en inglés) que tienen aplicación en los sistemas de industria 4.0. Los PMSM están incluidos en un conjunto de sistemas que deben tener la capacidad de diagnosticar su estado de operación y tomar decisiones para mantener la integridad de sus elementos en operación, evitando mantenimientos correctivos y paros de producción. Por tanto, se revisan trabajos de investigación, enfatizando aquellos de los últimos 10 años. En ellos se presentan las diferentes metodologías para el diagnóstico de fallas, tipos de fallas, algoritmos y elementos necesarios  para los PMSM. Con base en el análisis, queda manifiesta la gran relevancia del PMSM y el estudio de sus fallas para la industria 4.0.

Palabras clave: Diagnóstico de Fallas, Métodos de Detección, PMSM.


Abstract

This paper presents the state of the art of the existing research in the methodology for the diagnosis of faults in permanent magnet synchronous motors (PMSM) with application in industry 4.0 systems. The PMSM are included in a set of systems that must have the ability to diagnose their own operating status and make decisions to maintain the integrity of their elements in operation, avoiding corrective maintenance and production stoppages. Therefore, research works are reviewed, emphasizing those of the last 10 years. Different methodologies for the diagnosis of faults, types of faults, algorithms and elements necessary for this type of electric machine are presented. Based on the analysis, it is evident the great relevance of the PMSM and the study of its faults for the industry 4.0.

Keywords: Fault Diagnosis, Detection Methods, PMSM.


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Referencias


Abassi, M., Khlaief, A., Saadaoui, O., Chaari, A., & Boussak, M. (2016). Fault tolerant control and reconfiguration for three-phase permanent magnet synchronous motors drive,. 2016 4th International Conference on Control Engineering & Information Technology (CEIT), (pp. 1-6). Hammamet. doi:10.1109/CEIT.2016.7929053.

Ahsanullah, K., Jeyasankar, E,. Panda, S. K., Shanmukha, R. and Nadarajan, S. (2017) .Detection and analysis of winding and demagnetization faults in PMSM based marine propulsion motors, 2017 IEEE International Electric Machines and Drives Conference (IEMDC), Miami, FL,pp. 1-7. Doi:10.1109/IEMDC.2017.8002050.

Allouche, A., Etien, E., Doget, T., Rambault, L., Sakout, A., Cauet, S., & Martin, P. (2018). A PLL based mechanical faults detection in PMSM at variable speed, IFAC-PapersOnLine, Volume 51, Issue 24, pp 1445-1451, doi: 10.1016/j.ifacol.2018.09.534.

Bassi, L. (2017). Industry 4.0: Hope, hype or revolution?,. 2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI), pp. 1-6. doi: 10.1109/RTSI.2017.8065927.

Blanco, R., Fontodrona, J., & Poveda, C. (2017). La industria 4.0: El estado de la cuestión. Economía industrial, p.p. 151-164.

Chen, B., Wan, J., Shu, Y., Li, P., Mukherjee, M., & Yin, B. (2018) Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges, in IEEE Access, vol. 6, pp. 6505-6519, 2018.

doi: 10.1109/ACCESS.2017.2783682.

Choi, S. (2018). Fault Diagnosis Techniques for Permanent Magnet AC Machine and Drives—A Review of Current State of the Art,. in IEEE Transactions on Transportation Electrification, 4, 444-463. doi:10.1109/TTE.2018.2819627.

Chou, H. H., Kung, Y. S., Quynh, N. V., & Stone , C. (2013). Optimized FPGA design, verification and implementation of a neuro-fuzzy controller for PMSM drives. Mathematics and Computers in Simulation, 90, 28-44. Retrieved mayo 13, 2019, from https://doi.org/10.1016/j.matcom.2012.07.012.

Chuang, C., Wei, Z., Zhifu, W., & Zhi, L. (2017). The Diagnosis Method of Stator Winding Faults in PMSMs Based on SOM Neural Networks, Energy Procedia,Volume 105, pp 2295-2301, doi: 10.1016/j.egypro.2017.03.663.

Çıra, F. (2018). Detection of eccentricity fault based on vibration in the PMSM. Results in Physics, 10, 760-765. Retrieved from https://doi.org/10.1016/j.rinp.2018.06.044.

Di, C., Bao, X. Wang, H., Lv, Q. and He, Y. (2015). Modeling and Analysis of Unbalanced Magnetic Pull in Cage Induction Motors With Curved Dynamic Eccentricity, in IEEE Transactions on Magnetics, vol. 51, no. 8, pp. 1-7, Art no. 8106507. Doi: 10.1109/TMAG.2015.2412911

De Bisschop, J., Vansompel, H., Sergeant, P., & Dupre, L., (2017) Demagnetization Fault Detection in Axial Flux PM Machines by Using Sensing Coils and an Analytical Model, in IEEE Transactions on Magnetics, vol. 53, no. 6, pp. 1-4,, doi: 10.1109/TMAG.2017.2669480.

Dalenogare, L.S., Benitez, G.B., Ayala, N.A. & Frank, A.G. (2018) The expected contribution of Industry 4.0 technologies for industrial performance, International Journal of Production Economics, Volume 204, pp 383-394, doi: 10.1016/j.ijpe.2018.08.019

Ebrahimi, B. M., Faiz J. & Roshtkhari, M. J. (2009). Static-, Dynamic-, and Mixed-Eccentricity Fault Diagnoses in Permanent-Magnet Synchronous Motors, in IEEE Transactions on Industrial Electronics, vol. 56, no. 11, pp. 4727-4739.

Ebrahimi, B. M. and Faiz J. (2010). Diagnosis and performance analysis of threephase permanent magnet synchronous motors with static, dynamic and mixed eccentricity, in IET Electric Power Applications, vol. 4, no. 1, pp. 53-66.

Erdoğan, G. (2019). Land selection criteria for lights out factory districts during the industry 4.0 process, Journal of Urban Management, doi: 10.1016/j.jum.2019.01.001.

Esteban, E., Salgado, O., Iturrospe, A., & Isasa, I (2017). Design methodology of a reduced-scale test bench for fault detection and diagnosis, Mechatronics, Volume 47, pp 14-23, doi: 10.1016/j.mechatronics.2017.08.005.

Faiz, J., Exiri , A. H., & Nejadi-Koti, H. (2016). Current-based inter-turn short circuit fault modeling in permanent magnet synchronous machine using magnetic equivalent circuit model. 10th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG), (pp. 265-270). Bydgoszcz. doi:10.1109/CPE.2016.7544197

Flores, R., & Tomás I., A. (2011). Diagnóstico de Fallas en Máquinas Eléctricas Rotatorias Utilizando la Técnica de Espectros de Frecuencia de Bandas Laterales. Información Tecnológica, 73-84.

German-Sallo, Z., & Strnad, G. (2019), Machinery Fault Diagnosis Using Signal Analysis, Procedia Manufacturing, Volume 32, 2019, pp 585-590, doi: 10.1016/j.promfg.2019.02.256.

Goktas, T., Zafarani, M. and Akin, B. (2015) Separation of broken

magnet and static eccentricity failures in PMSM, 2015 IEEE International Electric Machines & Drives Conference (IEMDC), Coeur d'Alene, ID, pp. 1459-1465. Doi: 10.1109/IEMDC.2015.7409254.

Hang, J., Ding, S., Zhang, J., Cheng, M., Chen, W., & Wang, Q. (2016) Detection of Interturn Short-Circuit Fault for PMSM With Simple Fault Indicator, IEEE Transactions on Energy Conversion, vol. 31, no. 4, pp. 1697-1699, doi: 10.1109/TEC.2016.2583780

Jay, L., Bagheri, B., & Kao, H.-A. (2014). Recent Advances and Trends of Cyber-Phisycal Systems and Big Data Analytics in Industrial Informatics. Conference on Industrial Informatics (INDIN). Porto Alegre, Brazil.

Lin, T., Chen, Y., Yang, D., & Chen, Y. (2016). New Method for Industry 4.0 Machine Status Prediction - A Case Study with the Machine of a Spring Factory, 2016 International Computer Symposium (ICS), pp. 322-326.

doi: 10.1109/ICS.2016.0071

Luo, Y., Qiu, J., & Shi, C. (2018). Fault Detection of Permanent Magnet Synchronous Motor Based on Deep Learning Method. 21st International Conference on Electrical Machines and Systems (ICEMS), (pp. 699-703). Jeju. doi:10.23919/ICEMS.2018.8549129

Manavalan, E. & Jayakrishna, K. (2018). A review of Internet of Things (IoT) embedded sustainable supply chain for industry 4.0 requirements, Computers & Industrial Engineering, Volume 127, pp 925-953, doi: 10.1016/j.cie.2018.11.030.

Mehrjou, M. R., Mariun, N., Marhaban, M. H., & Misron, N. (2011). Rotor fault condition monitoring techniques for squirrel-cage induction machine—A review. Mechanical Systems and Signal Processing, 25, 2827-2848.

Nejadi-Koti, H., Faiz, J., & Demerdash, N. A. (2017). Uniform demagnetization fault diagnosis in permanent magnet synchronous motors by means of cogging torque analysis. IEEE International Electric Machines and Drives Conference (IEMDC), (pp. 1-7). Miami. doi:10.1109/IEMDC.2017.8002299

Otava, L., & Buchta, L. (2017). Implementation and verification of the PMSM stator interturn short fault detection algorithm. 19th European Conference on Power Electronics and Applications (EPE'17 ECCE Europe). Warsaw.

Pacas, M., Villwock, S., & Dietrich, R. (2019). Bearing damage detection in permanent magnet synchronous machines. EEE Energy Conversion Congress and Exposition. San Jose, CA.

Park, Y (2018). Online Detection of Rotor Eccentricity and Demagnetization Faults in PMSMs Based on Hall-Effect Field Sensor Measurements, IEEE Transactions on Industry Applications, vol. 55, no. 3, pp. 2499-2509, doi: 10.1109/TIA.2018.2886772.

Peralta, G., Iglesias-Urkia, M., Barcelo, M., Gomez, R., Moran, A., & Bilbao, J. (2017), Fog computing based efficient IoT scheme for the Industry 4.0, 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their Application to Mechatronics (ECMSM), pp. 1-6.

doi: 10.1109/ECMSM.2017.7945879.

Polat, A., Ertuğrul, Y. D. & Ergene, L. T. (2015), Static, dynamic and mixed eccentricity of induction motor, 2015 IEEE 10th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), Guarda, pp. 284-288.doi: 10.1109/DEMPED.2015.7303703.

Ramadan, M. (2019). Industry 4.0: Development of Smart Sunroof Ambient Light Manufacturing System for Automotive Industry, 2019 Advances in Science and Engineering Technology International Conferences (ASET), pp. 1-5.doi: 10.1109/ICASET.2019.8714236.

Román, J. L. (2016). Industria 4.0: la transformación digital de la industria. Facultad de infeniería de la Universidad de Deusto. España: CONFERENCIA DE DIRECTORES Y DECANOS DE INGENIERÍA INFORMÁTICA. Retrieved Febrero 04, 2019, from http://coddii.org/wp-content/uploads/2016/10/Informe-CODDII-Industria-4.0.pdf

Rosero, J. A., Cusido, J., Garcia, A., & Ortega, J. A. (2006). Broken Bearings and Eccentricity Fault Detection for a Permanent Magnet Synchronous Motor. IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics. Paris.

Rosero, J., Cusido, A., Espinosa, A. G., Ortega, J. A., r, & Romeral, L. (2007). Broken Bearings Fault Detection for a Permanent Magnet Synchronous Motor under non-constant working conditions by means of a Joint Time Frequency Analysis. IEEE International Symposium on Industrial Electronics. Vigo.

Saavedra, H., Urresty, J. C., Riba, J. R., & Romeral, L. (2014). Detection of interturn faults in PMSMs with different winding configurations. Energy Conversion and Management, 79, 534-542. Retrieved 05 15, 2019, from https://doi.org/10.1016/j.enconman.2013.12.059.

Smit, J., Industry 4.0. Directorate General for Internal Policies.

European Parliament.

Tapia, V. (2014). Industria 4.0 – Internet de las Cosas. UTCiencia.

Ciencia y tecnología al servicio del pueblo, 51-61.

Wang, C., Delgado Prieto, M., Romeral, L., Chen, Z., Blaabjerg, F., and Liu, X. (2016). Detection of Partial Demagnetization Fault in PMSMs Operating Under Nonstationary Conditions, in IEEE Transactions on Magnetics, vol. 52, no. 7, pp. 1-4. Doi: 10.1109/TMAG.2015.2511003.

Wangguang, Z. Wang, D. Wang, Y. Li and M. Li, (2017). A review on fault-tolerant control of PMSM, 2017 Chinese Automation Congress (CAC), Jinan, pp. 3854-3859. Doi: 10.1109/CAC.2017.8243452.

Xu, C., Qiu, C. & Wu, X. (2017). Eccentricity faults diagnosis based on motor stray magnetic field signature analysis, 2017 Chinese Automation Congress (CAC), Jinan, pp. 5577-5582. Doi: 10.1109/CAC.2017.8243776.

Youssef, A., Khil, S., & Belkhodja, I. (2017). Open-circuit fault diagnosis and voltage sensor fault tolerant control of a single phase pulsed

width modulated rectifier, Mathematics and Computers in Simulation, Volume 131, pp 234-252, doi: 10.1016/j.matcom.2015.10.005.

Zafarani, M., Goktas, T. and Akin, B. (2015). A comprehensive

analysis of magnet defect faults in permanent magnet synchronous motors," 2015 IEEE Applied Power Electronics Conference and Exposition (APEC), Charlotte, NC, pp. 2779-2783. Doi: 10.1109/APEC.2015.7104743.






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