On the Use of Positive Sequence Current / Negative Sequence Current Ratio for Fault Detection in Induction Motors
This paper studied the use of a new stator current feature for detection of winding and cage bars faults in an induction motor, and presents the experimental validation of a detection and identification scheme using Support Vector Machines (SVM). This validation was performed in a test bed using 2 H...
- Autores:
-
Castillo, Silvia Oviedo
Méndez, Jabid Quiroga
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2019
- Institución:
- Universidad EIA .
- Repositorio:
- Repositorio EIA .
- Idioma:
- spa
- OAI Identifier:
- oai:repository.eia.edu.co:11190/4945
- Acceso en línea:
- https://repository.eia.edu.co/handle/11190/4945
https://doi.org/10.24050/reia.v16i31.760
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- openAccess
- License
- Revista EIA - 2019
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On the Use of Positive Sequence Current / Negative Sequence Current Ratio for Fault Detection in Induction Motors |
| dc.title.translated.eng.fl_str_mv |
On the Use of Positive Sequence Current / Negative Sequence Current Ratio for Fault Detection in Induction Motors |
| title |
On the Use of Positive Sequence Current / Negative Sequence Current Ratio for Fault Detection in Induction Motors |
| spellingShingle |
On the Use of Positive Sequence Current / Negative Sequence Current Ratio for Fault Detection in Induction Motors |
| title_short |
On the Use of Positive Sequence Current / Negative Sequence Current Ratio for Fault Detection in Induction Motors |
| title_full |
On the Use of Positive Sequence Current / Negative Sequence Current Ratio for Fault Detection in Induction Motors |
| title_fullStr |
On the Use of Positive Sequence Current / Negative Sequence Current Ratio for Fault Detection in Induction Motors |
| title_full_unstemmed |
On the Use of Positive Sequence Current / Negative Sequence Current Ratio for Fault Detection in Induction Motors |
| title_sort |
On the Use of Positive Sequence Current / Negative Sequence Current Ratio for Fault Detection in Induction Motors |
| dc.creator.fl_str_mv |
Castillo, Silvia Oviedo Méndez, Jabid Quiroga |
| dc.contributor.author.spa.fl_str_mv |
Castillo, Silvia Oviedo Méndez, Jabid Quiroga |
| description |
This paper studied the use of a new stator current feature for detection of winding and cage bars faults in an induction motor, and presents the experimental validation of a detection and identification scheme using Support Vector Machines (SVM). This validation was performed in a test bed using 2 HP, 4 pole motors in which shorted winding and broken bars faults were induced, separately. Both time and frequency domain features like arithmetic mean, RMS value, Central Frequency, Kurtosis, RMS value of Power Spectral Density were assessed and validated using experimental data for several load conditions. PSC/NSC (positive sequence current/ negative sequence current) ratio was successful in most of the classifiers despite the load regime. This new feature was evaluated in terms of fault detection and severity discrimination with satisfactory results. |
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2019-01-20 00:00:00 2022-06-17T20:18:50Z |
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2019-01-20 |
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Awadallah, M.A.; Morcos, M.M.; (2004), ANFIS-based diagnosis and location of stator interturn faults in PM brushless DC motors, Energy Conversion, IEEE Transactions on , vol.19, no.4, pp. 795- 796. Bellini, A.; Concari, C.; Franceschini, G.; Lorenzani, E.; Tassoni, C.; Toscani, A.; (2006) , Thorough Understanding and Experimental Validation of Current Sideband Components in Induction Machines Rotor Monitoring, IEEE Industrial Electronics, IECON 2006 - 32nd Annual Conference on , vol., no., pp.4957-4962, 6-10. Bollen, H. M. and GU, I., (2006). Signal Processing of Power Quality Disturbances, IEEE Press Series on Power Engineering, p.861. Bouzid, M.; Champenois, G., (2013) Experimental compensation of the negative sequence current for accurate stator fault detection in induction motors, Industrial Electronics Society, IECON 2013 - 39th Annual Conference of the IEEE , vol., no., pp.2804,2809. Bouzida, A; Touhami, O.; Ibtiouen, R.; Belouchrani, A; Fadel, M.; Rezzoug, A, (2011). Fault Diagnosis in Industrial Induction Machines through Discrete Wavelet Transform, Industrial Electronics, IEEE Transactions on, vol.58, no.9, pp.4385, 4395. De Jesus Rangel-Magdaleno, J.; Peregrina-Barreto, H.; Ramirez-Cortes, J.M.; Gomez-Gil, P.; Morales-Caporal, R., (2014) FPGA-Based Broken Bars Detection on Induction Motors Under Different Load Using Motor Current Signature Analysis and Mathematical Morphology, Instrumentation and Measurement, IEEE Transactions on , vol.63, no.5, pp.1032,1040 Deraemaeker, A. (2006). Vibration based SHM: Comparison of the performance of modal features vs features extracted from spatial filters under changing environmental conditions. ISMA2006 International Conference on Noise and Vibration Engineering. p 849-864 . Dias, C.G.; Chabu, IE., (2014) Spectral Analysis Using a Hall Effect Sensor for Diagnosing Broken Bars in Large Induction Motors, Instrumentation and Measurement, IEEE Transactions on , vol.PP, no.99, pp.1,1. Ghate, V.N.; Dudul, S.V.; (2009), Fault Diagnosis of Three Phase Induction Motor Using Neural Network Techniques, Emerging Trends in Engineering and Technology (ICETET), 2009 2nd International Conference on, vol., no., pp.922-928, 16-18. Ghate, V.N.; Dudul, S.V. (2011) Cascade Neural-Network-Based Fault Classifier for Three-Phase Induction Motor, Industrial Electronics, IEEE Transactions on , vol.58, no.5, pp.1555-1563. Gordi, M., Roshanferk, R. (2010) A New Approach for Fault Detection of Broken Rotor Bars in Induction Motor Based on Support Vector Machine. Electrical Engineering (ICEE) 18th Iranian Conference on. vol., no., pp. 732,738, 11-13. Ng. Andrew. CS229 Lecture notes. Part IV. Standford University.Available:http://www.stanford.edu/class/cs229/materials.html. Nordin, N., Singh, H., (2014). Detection and classification of induction motor faults using Motor Current Signature Analysis and Multilayer Perceptron, Power Engineering and Optimization Conference (PEOCO), 2014 IEEE 8th International, vol., no., pp.35-40, 24-25. Oviedo, S.; Quiroga, J. and Ordoñez, G. (2014) Validación Experimental de la Metodología Motor Current Signature Analysis para un Motor de Inducción de 2 HP. Rev.fac.ing.univ. Antioquia. Vol., no.70, pp. 108,118. Oviedo, S.; Quiroga, J., Borras, C. (2011) Motor current signature analysis and negative sequence current based stator winding short fault detection in an induction motor. Dyna rev.fac.nac.minas, vol.78, n.170, pp. 214-220. Oviedo, S. J.; Quiroga, J. E.; Borras, C. (2011) Experimental evaluation of motor current signature and vibration analysis for rotor broken bars detection in an induction motor, Power Engineering, Energy and Electrical Drives (POWERENG), 2011 International Conference on , vol., no., pp.1,6. Penrose, Howard (2004). Applications for motor current. s.l. : ALL-TEST Pro White Paper. Poncelas, O.; Rosero, J.A.; Cusido, J.; Ortega, J.A.; Romeral, L.; (2008), Design and application of Rogowski coil current sensor without integrator for fault detection in induction motors, Industrial Electronics, 2008. ISIE 2008. IEEE International Symposium on, vol., no., pp.558-563. Puche-Panadero, R.; Pineda-Sanchez, M.; Riera-Guasp, M.; Roger-Folch, J.; Hurtado-Perez, E.; Perez-Cruz, J. (2009), Improved Resolution of the MCSA Method Via Hilbert Transform, Enabling the Diagnosis of Rotor Asymmetries at Very Low Slip, Energy Conversion, IEEE Transactions on , vol.24, no.1, pp.52,59. Quiroga, J (2010). Stator winding short-circuit fault detection in a permanent magnet synchronous motor (PMSM) using negative sequence current in time domain, Ingeniería e Investigación, vol.29, no. 2, pp. 48,52. Scholkopf, B. and J., Smola A. (2002) Learning with Kernels. MIT Press. Cambridge, Massachusetts. Theodoridis, S. and Koutroumbas, K. Pattern Recognition. (2009) 4 Ed. Elsevier. p. 412-414. Theodoridis, S., et al. Introduction to Pattern Recorgnition. A Matlab Approach. (2010) 4 ed. Elsevier, Academic Press. p. 107-122. Teotrakool, K.; Devaney, M.J.; Eren, L. (2006); Adjustable Speed Drive Bearing Fault Detection via Wavelet Packet Decomposition, Instrumentation and Measurement Technology Conference. IMTC 2006. Proceedings of the IEEE, vol., no., pp.22-25. Thomson, W.T. and Fenger, M. (2001). Current signature analysis to detect induction motor faults, Industry Applications Magazine, vol.7, No.4, Jul/Aug. p. 26-34. Thomson, W.T.; Fenger, M. (2003), Case histories of current signature analysis to detect faults in induction motor drives, Electric Machines and Drives Conference, 2003. IEMDC'03. IEEE International, vol.3, 1-4, pp. 1459- 1465. Thomson, W.T. (1994) On-line current monitoring to detect electrical and mechanical faults in three-phase induction motor drives. Life Management of Power Plants, 1994, International Conference on, p. 66-73. Widodo, A,., Yang, B.S., Han, T. (2007) Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motor. Expert Systems, vol 32, no., pp. 299,312. |
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Castillo, Silvia Oviedo3d1f48cace199f50797a91b9f46ab1d0300Méndez, Jabid Quirogae2f732871846d5bd9be5e249812ec73c3002019-01-20 00:00:002022-06-17T20:18:50Z2019-01-20 00:00:002022-06-17T20:18:50Z2019-01-201794-1237https://repository.eia.edu.co/handle/11190/494510.24050/reia.v16i31.7602463-0950https://doi.org/10.24050/reia.v16i31.760This paper studied the use of a new stator current feature for detection of winding and cage bars faults in an induction motor, and presents the experimental validation of a detection and identification scheme using Support Vector Machines (SVM). This validation was performed in a test bed using 2 HP, 4 pole motors in which shorted winding and broken bars faults were induced, separately. Both time and frequency domain features like arithmetic mean, RMS value, Central Frequency, Kurtosis, RMS value of Power Spectral Density were assessed and validated using experimental data for several load conditions. PSC/NSC (positive sequence current/ negative sequence current) ratio was successful in most of the classifiers despite the load regime. This new feature was evaluated in terms of fault detection and severity discrimination with satisfactory results.This paper studied the use of a new stator current feature for detection of winding and cage bars faults in an induction motor, and presents the experimental validation of a detection and identification scheme using Support Vector Machines (SVM). This validation was performed in a test bed using 2 HP, 4 pole motors in which shorted winding and broken bars faults were induced, separately. Both time and frequency domain features like arithmetic mean, RMS value, Central Frequency, Kurtosis, RMS value of Power Spectral Density were assessed and validated using experimental data for several load conditions. PSC/NSC (positive sequence current/ negative sequence current) ratio was successful in most of the classifiers despite the load regime. This new feature was evaluated in terms of fault detection and severity discrimination with satisfactory results.application/pdfspaFondo Editorial EIA - Universidad EIARevista EIA - 2019https://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2https://revistas.eia.edu.co/index.php/reveia/article/view/760On the Use of Positive Sequence Current / Negative Sequence Current Ratio for Fault Detection in Induction MotorsOn the Use of Positive Sequence Current / Negative Sequence Current Ratio for Fault Detection in Induction MotorsArtículo de revistaJournal articlehttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionTexthttp://purl.org/redcol/resource_type/ARTREFhttp://purl.org/coar/version/c_970fb48d4fbd8a85Awadallah, M.A.; Morcos, M.M.; (2004), ANFIS-based diagnosis and location of stator interturn faults in PM brushless DC motors, Energy Conversion, IEEE Transactions on , vol.19, no.4, pp. 795- 796.Bellini, A.; Concari, C.; Franceschini, G.; Lorenzani, E.; Tassoni, C.; Toscani, A.; (2006) , Thorough Understanding and Experimental Validation of Current Sideband Components in Induction Machines Rotor Monitoring, IEEE Industrial Electronics, IECON 2006 - 32nd Annual Conference on , vol., no., pp.4957-4962, 6-10.Bollen, H. M. and GU, I., (2006). Signal Processing of Power Quality Disturbances, IEEE Press Series on Power Engineering, p.861.Bouzid, M.; Champenois, G., (2013) Experimental compensation of the negative sequence current for accurate stator fault detection in induction motors, Industrial Electronics Society, IECON 2013 - 39th Annual Conference of the IEEE , vol., no., pp.2804,2809.Bouzida, A; Touhami, O.; Ibtiouen, R.; Belouchrani, A; Fadel, M.; Rezzoug, A, (2011). Fault Diagnosis in Industrial Induction Machines through Discrete Wavelet Transform, Industrial Electronics, IEEE Transactions on, vol.58, no.9, pp.4385, 4395.De Jesus Rangel-Magdaleno, J.; Peregrina-Barreto, H.; Ramirez-Cortes, J.M.; Gomez-Gil, P.; Morales-Caporal, R., (2014) FPGA-Based Broken Bars Detection on Induction Motors Under Different Load Using Motor Current Signature Analysis and Mathematical Morphology, Instrumentation and Measurement, IEEE Transactions on , vol.63, no.5, pp.1032,1040Deraemaeker, A. (2006). Vibration based SHM: Comparison of the performance of modal features vs features extracted from spatial filters under changing environmental conditions. ISMA2006 International Conference on Noise and Vibration Engineering. p 849-864 . Dias, C.G.; Chabu, IE., (2014) Spectral Analysis Using a Hall Effect Sensor for Diagnosing Broken Bars in Large Induction Motors, Instrumentation and Measurement, IEEE Transactions on , vol.PP, no.99, pp.1,1.Ghate, V.N.; Dudul, S.V.; (2009), Fault Diagnosis of Three Phase Induction Motor Using Neural Network Techniques, Emerging Trends in Engineering and Technology (ICETET), 2009 2nd International Conference on, vol., no., pp.922-928, 16-18.Ghate, V.N.; Dudul, S.V. (2011) Cascade Neural-Network-Based Fault Classifier for Three-Phase Induction Motor, Industrial Electronics, IEEE Transactions on , vol.58, no.5, pp.1555-1563.Gordi, M., Roshanferk, R. (2010) A New Approach for Fault Detection of Broken Rotor Bars in Induction Motor Based on Support Vector Machine. Electrical Engineering (ICEE) 18th Iranian Conference on. vol., no., pp. 732,738, 11-13.Ng. Andrew. CS229 Lecture notes. Part IV. Standford University.Available:http://www.stanford.edu/class/cs229/materials.html.Nordin, N., Singh, H., (2014). Detection and classification of induction motor faults using Motor Current Signature Analysis and Multilayer Perceptron, Power Engineering and Optimization Conference (PEOCO), 2014 IEEE 8th International, vol., no., pp.35-40, 24-25.Oviedo, S.; Quiroga, J. and Ordoñez, G. (2014) Validación Experimental de la Metodología Motor Current Signature Analysis para un Motor de Inducción de 2 HP. Rev.fac.ing.univ. Antioquia. Vol., no.70, pp. 108,118.Oviedo, S.; Quiroga, J., Borras, C. (2011) Motor current signature analysis and negative sequence current based stator winding short fault detection in an induction motor. Dyna rev.fac.nac.minas, vol.78, n.170, pp. 214-220.Oviedo, S. J.; Quiroga, J. E.; Borras, C. (2011) Experimental evaluation of motor current signature and vibration analysis for rotor broken bars detection in an induction motor, Power Engineering, Energy and Electrical Drives (POWERENG), 2011 International Conference on , vol., no., pp.1,6.Penrose, Howard (2004). Applications for motor current. s.l. : ALL-TEST Pro White Paper.Poncelas, O.; Rosero, J.A.; Cusido, J.; Ortega, J.A.; Romeral, L.; (2008), Design and application of Rogowski coil current sensor without integrator for fault detection in induction motors, Industrial Electronics, 2008. ISIE 2008. IEEE International Symposium on, vol., no., pp.558-563.Puche-Panadero, R.; Pineda-Sanchez, M.; Riera-Guasp, M.; Roger-Folch, J.; Hurtado-Perez, E.; Perez-Cruz, J. (2009), Improved Resolution of the MCSA Method Via Hilbert Transform, Enabling the Diagnosis of Rotor Asymmetries at Very Low Slip, Energy Conversion, IEEE Transactions on , vol.24, no.1, pp.52,59.Quiroga, J (2010). Stator winding short-circuit fault detection in a permanent magnet synchronous motor (PMSM) using negative sequence current in time domain, Ingeniería e Investigación, vol.29, no. 2, pp. 48,52.Scholkopf, B. and J., Smola A. (2002) Learning with Kernels. MIT Press. Cambridge, Massachusetts.Theodoridis, S. and Koutroumbas, K. Pattern Recognition. (2009) 4 Ed. Elsevier. p. 412-414.Theodoridis, S., et al. Introduction to Pattern Recorgnition. A Matlab Approach. (2010) 4 ed. Elsevier, Academic Press. p. 107-122.Teotrakool, K.; Devaney, M.J.; Eren, L. (2006); Adjustable Speed Drive Bearing Fault Detection via Wavelet Packet Decomposition, Instrumentation and Measurement Technology Conference. IMTC 2006. Proceedings of the IEEE, vol., no., pp.22-25.Thomson, W.T. and Fenger, M. (2001). Current signature analysis to detect induction motor faults, Industry Applications Magazine, vol.7, No.4, Jul/Aug. p. 26-34.Thomson, W.T.; Fenger, M. (2003), Case histories of current signature analysis to detect faults in induction motor drives, Electric Machines and Drives Conference, 2003. IEMDC'03. IEEE International, vol.3, 1-4, pp. 1459- 1465.Thomson, W.T. (1994) On-line current monitoring to detect electrical and mechanical faults in three-phase induction motor drives. Life Management of Power Plants, 1994, International Conference on, p. 66-73.Widodo, A,., Yang, B.S., Han, T. (2007) Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motor. Expert Systems, vol 32, no., pp. 299,312.https://revistas.eia.edu.co/index.php/reveia/article/download/760/1218Núm. 31 , Año 201956314316Revista EIAPublicationOREORE.xmltext/xml2637https://repository.eia.edu.co/bitstreams/772b4c2d-7180-4988-b571-88972b795683/download2084deaef381c3e1d08741640bd61052MD5111190/4945oai:repository.eia.edu.co:11190/49452023-07-25 17:22:45.331https://creativecommons.org/licenses/by-nc-sa/4.0/Revista EIA - 2019metadata.onlyhttps://repository.eia.edu.coRepositorio Institucional Universidad EIAbdigital@metabiblioteca.com |