On the Use of Positive Sequence Current / Negative Sequence Current Ratio for Fault Detection in Induction Motors

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 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... Ver más

Guardado en:

Revista EIA

1794-1237

2463-0950

Méndez, Jabid Quiroga

Castillo, Silvia Oviedo

UNIVERSIDAD EIA

10.24050/reia.v16i31.760

16

2019-01-20

43

56

Colombia

info:eu-repo/semantics/openAccess

http://purl.org/coar/access_right/c_abf2

Revista EIA - 2019

id c947df3c468da8ea6b214c137383ad2f
record_format ojs
spelling On the Use of Positive Sequence Current / Negative Sequence Current Ratio for Fault Detection in Induction Motors
Theodoridis, S., et al. Introduction to Pattern Recorgnition. A Matlab Approach. (2010) 4 ed. Elsevier, Academic Press. p. 107-122.
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.
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.
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.
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.
info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
http://purl.org/coar/resource_type/c_2df8fbb1
http://purl.org/redcol/resource_type/ART
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/version/c_970fb48d4fbd8a85
info:eu-repo/semantics/openAccess
http://purl.org/coar/access_right/c_abf2
Text
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.
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.
Publication
Español
application/pdf
Fondo Editorial EIA - Universidad EIA
Revista EIA
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
31
https://revistas.eia.edu.co/index.php/reveia/article/view/760
16
Méndez, Jabid Quiroga
https://creativecommons.org/licenses/by-nc-sa/4.0/
Revista EIA - 2019
Castillo, Silvia Oviedo
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.
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.
Artículo de revista
Journal article
On the Use of Positive Sequence Current / Negative Sequence Current Ratio for Fault Detection in Induction Motors
2019-01-20 00:00:00
2019-01-20 00:00:00
https://revistas.eia.edu.co/index.php/reveia/article/download/760/1218
2019-01-20
2463-0950
10.24050/reia.v16i31.760
https://doi.org/10.24050/reia.v16i31.760
43
56
1794-1237
institution UNIVERSIDAD EIA
thumbnail https://nuevo.metarevistas.org/UNIVERSIDADEIA/logo.png
country_str Colombia
collection Revista EIA
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
Méndez, Jabid Quiroga
Castillo, Silvia Oviedo
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
title_eng On the Use of Positive Sequence Current / Negative Sequence Current Ratio for Fault Detection in Induction Motors
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.
author Méndez, Jabid Quiroga
Castillo, Silvia Oviedo
author_facet Méndez, Jabid Quiroga
Castillo, Silvia Oviedo
citationvolume 16
citationissue 31
publisher Fondo Editorial EIA - Universidad EIA
ispartofjournal Revista EIA
source https://revistas.eia.edu.co/index.php/reveia/article/view/760
language Español
format Article
rights info:eu-repo/semantics/openAccess
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by-nc-sa/4.0/
Revista EIA - 2019
references Theodoridis, S., et al. Introduction to Pattern Recorgnition. A Matlab Approach. (2010) 4 ed. Elsevier, Academic Press. p. 107-122.
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.
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.
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.
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.
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.
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.
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
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.
type_driver info:eu-repo/semantics/article
type_coar http://purl.org/coar/resource_type/c_6501
type_version info:eu-repo/semantics/publishedVersion
type_coarversion http://purl.org/coar/version/c_970fb48d4fbd8a85
type_content Text
publishDate 2019-01-20
date_accessioned 2019-01-20 00:00:00
date_available 2019-01-20 00:00:00
url https://revistas.eia.edu.co/index.php/reveia/article/view/760
url_doi https://doi.org/10.24050/reia.v16i31.760
issn 1794-1237
eissn 2463-0950
doi 10.24050/reia.v16i31.760
citationstartpage 43
citationendpage 56
url3_str_mv https://revistas.eia.edu.co/index.php/reveia/article/download/760/1218
_version_ 1797159280687710208

Código QR

Estadísticas y Métricas Alternativas

Títulos similares