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Induction machine model with space harmonics for the diagnosis of rotor eccentricity, based on the convolution theorem

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Induction machine model with space harmonics for the diagnosis of rotor eccentricity, based on the convolution theorem

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dc.contributor.author Sapena-Bano, Angel es_ES
dc.contributor.author Martinez-Roman, Javier es_ES
dc.contributor.author Puche-Panadero, Rubén es_ES
dc.contributor.author Pineda Sánchez, Manuel es_ES
dc.contributor.author Pérez-Cruz, Juan es_ES
dc.contributor.author Riera-Guasp, Martín es_ES
dc.date.accessioned 2021-07-10T03:30:45Z
dc.date.available 2021-07-10T03:30:45Z
dc.date.issued 2020-05 es_ES
dc.identifier.issn 0142-0615 es_ES
dc.identifier.uri http://hdl.handle.net/10251/169060
dc.description.abstract [EN] Condition based maintenance (CBM) systems of induction machines (IMs) require fast and accurate models that can reproduce the fault related harmonics generated by different kinds of faults. Such models are needed to develop new diagnostic algorithms for detecting the faults at an early stage, to analyse the physical interactions between simultaneous faults of different types, or to train expert systems that can supervise and identify these faults in an autonomous way. To achieve these goals, these models must take into account the space harmonics of the air gap magnetomotive force (MMF) generated by the machine windings under fault conditions, due to the complex interactions between spatial and time harmonics in a faulty machine. One of the most common faults in induction machines is the rotor eccentricity, which can cause significant radial forces and, in extreme cases, produce destructive rotor-stator rub. However, the development of a fast, analytical model of the eccentric IM is a challenging task, due to the non-uniformity of the air gap. In this paper, a new method is proposed to obtain such a fast model. This method, which is theoretically justified, first enables a fast calculation of the self and mutual inductances of the stator and rotor phases for every rotor position, taking into account the non-uniform air-gap length and the actual position of all the stator and rotor conductors. Once these inductances are calculated, they are used in a coupled circuits analytical model of the IM, which in this way is able to calculate the time evolution of the electrical and mechanical quantities that characterize the machine functioning, under any type of eccentricity. Specifically, the model is able to reproduce accurately the characteristic eccentricity fault related harmonics in the spectrum of the stator current. The proposed approach is validated through two different methods. First, using a finite elements (FEM) model, in order to validate the correctness of the proposed method for calculating self and mutual inductances, taking into account the non-uniform air-gap. Finally, through an experimental test-bed using a commercial induction motor with a forced mixed eccentricity fault, in order to validate that the full model correctly reproduces the phase currents in such a way that their spectra accurately show the harmonics related with the eccentricity fault, which are the basis of many MCSA diagnostic approaches. es_ES
dc.description.sponsorship This work was supported by the Spanish "Ministerio de Ciencia, Innovacion y Universidades (MCIU)", the "Agenda Estatal de Investigacion (AEI)" and the "Fondo Europeo de Desarrollo Regional (FEDER)" in the framework of the "Proyectos I + D + i - Retos Investigacion 2018", project reference RTI2018-102175-13400 (MCIU/AEI/FEDER, UE). es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof International Journal of Electrical Power & Energy Systems es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Inductance es_ES
dc.subject Induction machines es_ES
dc.subject Convolution es_ES
dc.subject Discrete Fourier transforms es_ES
dc.subject Fault diagnosis es_ES
dc.subject Air gap eccentricity es_ES
dc.subject.classification INGENIERIA ELECTRICA es_ES
dc.title Induction machine model with space harmonics for the diagnosis of rotor eccentricity, based on the convolution theorem es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.ijepes.2019.105625 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-102175-B-I00/ES/DISEÑO DE MODELOS AVANZADOS DE SIMULACION DE AEROGENERADORES PARA EL DESARROLLO Y PUESTA A PUNTO DE SISTEMAS DE DIAGNOSTICO DE AVERIAS "ON-LINE"./ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Eléctrica - Departament d'Enginyeria Elèctrica es_ES
dc.description.bibliographicCitation Sapena-Bano, A.; Martinez-Roman, J.; Puche-Panadero, R.; Pineda Sánchez, M.; Pérez-Cruz, J.; Riera-Guasp, M. (2020). Induction machine model with space harmonics for the diagnosis of rotor eccentricity, based on the convolution theorem. International Journal of Electrical Power & Energy Systems. 117:1-19. https://doi.org/10.1016/j.ijepes.2019.105625 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.ijepes.2019.105625 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 19 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 117 es_ES
dc.relation.pasarela S\400261 es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.description.references Liu, Y., & Bazzi, A. M. (2017). A review and comparison of fault detection and diagnosis methods for squirrel-cage induction motors: State of the art. ISA Transactions, 70, 400-409. doi:10.1016/j.isatra.2017.06.001 es_ES
dc.description.references Balasubramanian, A., & Muthu, R. (2017). Model Based Fault Detection and Diagnosis of Doubly Fed Induction Generators – A Review. Energy Procedia, 117, 935-942. doi:10.1016/j.egypro.2017.05.213 es_ES
dc.description.references Bellini, A., Filippetti, F., Tassoni, C., & Capolino, G.-A. (2008). Advances in Diagnostic Techniques for Induction Machines. IEEE Transactions on Industrial Electronics, 55(12), 4109-4126. doi:10.1109/tie.2008.2007527 es_ES
dc.description.references Culbert, I., & Letal, J. (2017). Signature Analysis for Online Motor Diagnostics: Early Detection of Rotating Machine Problems Prior to Failure. IEEE Industry Applications Magazine, 23(4), 76-81. doi:10.1109/mias.2016.2600684 es_ES
dc.description.references Manohar, M., Koley, E., & Ghosh, S. (2019). Enhancing resilience of PV-fed microgrid by improved relaying and differentiating between inverter faults and distribution line faults. International Journal of Electrical Power & Energy Systems, 108, 271-279. doi:10.1016/j.ijepes.2019.01.015 es_ES
dc.description.references Xia, X., Zhou, J., Li, C., & Zhu, W. (2015). A novel method for fault diagnosis of hydro generator based on NOFRFs. International Journal of Electrical Power & Energy Systems, 71, 60-67. doi:10.1016/j.ijepes.2015.02.022 es_ES
dc.description.references Zhao, B., Yang, M., Diao, H. R., An, B., Zhao, Y. C., & Zhang, Y. M. (2019). A novel approach to transformer fault diagnosis using IDM and naive credal classifier. International Journal of Electrical Power & Energy Systems, 105, 846-855. doi:10.1016/j.ijepes.2018.09.029 es_ES
dc.description.references Zhang, Y., Zhang, Y., Wen, F., Chung, C. Y., Tseng, C.-L., Zhang, X., … Yuan, Y. (2016). A fuzzy Petri net based approach for fault diagnosis in power systems considering temporal constraints. International Journal of Electrical Power & Energy Systems, 78, 215-224. doi:10.1016/j.ijepes.2015.11.095 es_ES
dc.description.references Farshad, M. (2019). Detection and classification of internal faults in bipolar HVDC transmission lines based on K-means data description method. International Journal of Electrical Power & Energy Systems, 104, 615-625. doi:10.1016/j.ijepes.2018.07.044 es_ES
dc.description.references Yahia, K., Sahraoui, M., Cardoso, A. J. M., & Ghoggal, A. (2016). The Use of a Modified Prony’s Method to Detect the Airgap-Eccentricity Occurrence in Induction Motors. IEEE Transactions on Industry Applications, 52(5), 3869-3877. doi:10.1109/tia.2016.2582146 es_ES
dc.description.references El Bouchikhi, E. H., Choqueuse, V., & Benbouzid, M. (2015). Induction machine faults detection using stator current parametric spectral estimation. Mechanical Systems and Signal Processing, 52-53, 447-464. doi:10.1016/j.ymssp.2014.06.015 es_ES
dc.description.references Morales-Perez, C., Rangel-Magdaleno, J., Peregrina-Barreto, H., Amezquita-Sanchez, J. P., & Valtierra-Rodriguez, M. (2018). Incipient Broken Rotor Bar Detection in Induction Motors Using Vibration Signals and the Orthogonal Matching Pursuit Algorithm. IEEE Transactions on Instrumentation and Measurement, 67(9), 2058-2068. doi:10.1109/tim.2018.2813820 es_ES
dc.description.references Faiz, J., & Ojaghi, M. (2009). Instantaneous-Power Harmonics as Indexes for Mixed Eccentricity Fault in Mains-Fed and Open/Closed-Loop Drive-Connected Squirrel-Cage Induction Motors. IEEE Transactions on Industrial Electronics, 56(11), 4718-4726. doi:10.1109/tie.2009.2030816 es_ES
dc.description.references Oumaamar, M. E. K., Maouche, Y., Boucherma, M., & Khezzar, A. (2017). Static air-gap eccentricity fault diagnosis using rotor slot harmonics in line neutral voltage of three-phase squirrel cage induction motor. Mechanical Systems and Signal Processing, 84, 584-597. doi:10.1016/j.ymssp.2016.07.016 es_ES
dc.description.references Resendiz-Ochoa, E., Osornio-Rios, R. A., Benitez-Rangel, J. P., De J. Romero-Troncoso, R., & Morales-Hernandez, L. A. (2018). Induction Motor Failure Analysis: An Automatic Methodology Based on Infrared Imaging. IEEE Access, 6, 76993-77003. doi:10.1109/access.2018.2883988 es_ES
dc.description.references Mirzaeva, G., & Saad, K. I. (2018). Advanced Diagnosis of Stator Turn-to-Turn Faults and Static Eccentricity in Induction Motors Based on Internal Flux Measurement. IEEE Transactions on Industry Applications, 54(4), 3961-3970. doi:10.1109/tia.2018.2821098 es_ES
dc.description.references Sangeetha B., P., & S., H. (2019). Rational-Dilation Wavelet Transform Based Torque Estimation from Acoustic Signals for Fault Diagnosis in a Three-Phase Induction Motor. IEEE Transactions on Industrial Informatics, 15(6), 3492-3501. doi:10.1109/tii.2018.2874463 es_ES
dc.description.references Naha, A., Samanta, A. K., Routray, A., & Deb, A. K. (2017). Low Complexity Motor Current Signature Analysis Using Sub-Nyquist Strategy With Reduced Data Length. IEEE Transactions on Instrumentation and Measurement, 66(12), 3249-3259. doi:10.1109/tim.2017.2737879 es_ES
dc.description.references Kaikaa, M. Y., Hadjami, M., & Khezzar, A. (2014). Effects of the simultaneous presence of static eccentricity and broken rotor bars on the stator current of induction machine. IEEE Transactions on Industrial Electronics, 61(5), 2452-2463. doi:10.1109/tie.2013.2270216 es_ES
dc.description.references Singh, G., & Naikan, V. N. A. (2018). Detection of half broken rotor bar fault in VFD driven induction motor drive using motor square current MUSIC analysis. Mechanical Systems and Signal Processing, 110, 333-348. doi:10.1016/j.ymssp.2018.03.001 es_ES
dc.description.references Singh, A., Grant, B., DeFour, R., Sharma, C., & Bahadoorsingh, S. (2016). A review of induction motor fault modeling. Electric Power Systems Research, 133, 191-197. doi:10.1016/j.epsr.2015.12.017 es_ES
dc.description.references Gangsar, P., & Tiwari, R. (2019). A support vector machine based fault diagnostics of Induction motors for practical situation of multi-sensor limited data case. Measurement, 135, 694-711. doi:10.1016/j.measurement.2018.12.011 es_ES
dc.description.references Ojaghi, M., Aghmasheh, R., & Sabouri, M. (2016). Model‐based exact technique to identify type and degree of eccentricity faults in induction motors. IET Electric Power Applications, 10(8), 706-713. doi:10.1049/iet-epa.2016.0026 es_ES
dc.description.references Salah, A. A., Dorrell, D. G., & Guo, Y. (2019). A Review of the Monitoring and Damping Unbalanced Magnetic Pull in Induction Machines Due to Rotor Eccentricity. IEEE Transactions on Industry Applications, 55(3), 2569-2580. doi:10.1109/tia.2019.2892359 es_ES
dc.description.references Faiz, J., & Moosavi, S. M. M. (2017). Detection of mixed eccentricity fault in doubly‐fed induction generator based on reactive power spectrum. IET Electric Power Applications, 11(6), 1076-1084. doi:10.1049/iet-epa.2016.0449 es_ES
dc.description.references Naderi, P., & Fallahi, F. (2016). Eccentricity fault diagnosis in three-phase-wound-rotor induction machine using numerical discrete modeling method. International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, 29(5), 982-997. doi:10.1002/jnm.2157 es_ES
dc.description.references Faiz, J., & Moosavi, S. M. M. (2016). Eccentricity fault detection – From induction machines to DFIG—A review. Renewable and Sustainable Energy Reviews, 55, 169-179. doi:10.1016/j.rser.2015.10.113 es_ES
dc.description.references Silwal, B., Rasilo, P., Perkkio, L., Hannukainen, A., Eirola, T., & Arkkio, A. (2016). Numerical Analysis of the Power Balance of an Electrical Machine With Rotor Eccentricity. IEEE Transactions on Magnetics, 52(3), 1-4. doi:10.1109/tmag.2015.2477847 es_ES
dc.description.references Yao, Y., Li, Y., & Yin, Q. (2019). A novel method based on self-sensing motor drive system for misalignment detection. Mechanical Systems and Signal Processing, 116, 217-229. doi:10.1016/j.ymssp.2018.06.030 es_ES
dc.description.references Dorrell, D. G. (2011). Sources and Characteristics of Unbalanced Magnetic Pull in Three-Phase Cage Induction Motors With Axial-Varying Rotor Eccentricity. IEEE Transactions on Industry Applications, 47(1), 12-24. doi:10.1109/tia.2010.2090845 es_ES
dc.description.references Xu, X., Han, Q., & Chu, F. (2018). A general electromagnetic excitation model for electrical machines considering the magnetic saturation and rub impact. Journal of Sound and Vibration, 416, 154-171. doi:10.1016/j.jsv.2017.11.050 es_ES
dc.description.references Faiz, J., Ebrahimi, B. M., & Sharifian, M. B. B. (2007). Finite Element Transient Analysis of an On-Load Three-Phase Squirrel-Cage Induction Motor with Static Eccentricity. Electromagnetics, 27(4), 207-227. doi:10.1080/02726340701272154 es_ES
dc.description.references BeBortoli, M. J., Salon, S. J., Burow, D. W., & Slavik, C. J. (1993). Effects of rotor eccentricity and parallel windings on induction machine behavior: a study using finite element analysis. IEEE Transactions on Magnetics, 29(2), 1676-1682. doi:10.1109/20.250728 es_ES
dc.description.references Faiz, J., Ebrahimi, B. M., Akin, B., & Toliyat, H. A. (2009). Comprehensive Eccentricity Fault Diagnosis in Induction Motors Using Finite Element Method. IEEE Transactions on Magnetics, 45(3), 1764-1767. doi:10.1109/tmag.2009.2012812 es_ES
dc.description.references Martinez, J., Belahcen, A., Detoni, J., & Arkkio, A. (2013). A 2D FEM analysis of electromechanical signatures in induction motors under dynamic eccentricity. International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, 27(3), 555-571. doi:10.1002/jnm.1942 es_ES
dc.description.references Thiele, M., & Heins, G. (2016). Computationally Efficient Method for Identifying Manufacturing Induced Rotor and Stator Misalignment in Permanent Magnet Brushless Machines. IEEE Transactions on Industry Applications, 52(4), 3033-3040. doi:10.1109/tia.2016.2552145 es_ES
dc.description.references Ajily, E., Ardebili, M., & Abbaszadeh, K. (2016). Magnet Defect and Rotor Eccentricity Modeling in Axial-Flux Permanent-Magnet Machines via 3-D Field Reconstruction Method. IEEE Transactions on Energy Conversion, 31(2), 486-495. doi:10.1109/tec.2015.2506819 es_ES
dc.description.references Mahmoud, H., & Bianchi, N. (2015). Eccentricity in Synchronous Reluctance Motors—Part I: Analytical and Finite-Element Models. IEEE Transactions on Energy Conversion, 30(2), 745-753. doi:10.1109/tec.2014.2384535 es_ES
dc.description.references Faiz, J., Ghasemi-Bijan, M., & Mahdi Ebrahimi, B. (2015). Modeling and Diagnosing Eccentricity Fault Using Three-dimensional Magnetic Equivalent Circuit Model of Three-phase Squirrel-cage Induction Motor. Electric Power Components and Systems, 43(11), 1246-1256. doi:10.1080/15325008.2015.1029651 es_ES
dc.description.references Garg, H., & Dahiya, R. (2016). Current signature analysis and its application in the condition monitoring of wind turbine for rotor faults. Energy Systems, 8(3), 495-510. doi:10.1007/s12667-016-0208-6 es_ES
dc.description.references Bao, X., Cheng, Z., & Di, C. (2017). Current analysis of large submersible motor under curved eccentricity by multi-loop method. International Journal of Applied Electromagnetics and Mechanics, 53(1), 63-76. doi:10.3233/jae-160005 es_ES
dc.description.references Rajalakshmi Samaga, B. L., & Vittal, K. P. (2012). Comprehensive study of mixed eccentricity fault diagnosis in induction motors using signature analysis. International Journal of Electrical Power & Energy Systems, 35(1), 180-185. doi:10.1016/j.ijepes.2011.10.011 es_ES
dc.description.references Ghoggal, A., Zouzou, S. E., Razik, H., Sahraoui, M., & Khezzar, A. (2009). An improved model of induction motors for diagnosis purposes – Slot skewing effect and air–gap eccentricity faults. Energy Conversion and Management, 50(5), 1336-1347. doi:10.1016/j.enconman.2009.01.003 es_ES
dc.description.references Faiz, J., & Ojaghi, M. (2009). Unified winding function approach for dynamic simulation of different kinds of eccentricity faults in cage induction machines. IET Electric Power Applications, 3(5), 461. doi:10.1049/iet-epa.2008.0206 es_ES
dc.description.references Toliyat, H. A., Lipo, T. A., & White, J. C. (1991). Analysis of a concentrated winding induction machine for adjustable speed drive applications. I. Motor analysis. IEEE Transactions on Energy Conversion, 6(4), 679-683. doi:10.1109/60.103641 es_ES
dc.description.references Ghoggal A, Zouzou SE, Razik H, Sahraoui M, Hadri-Hamida A. Application of the convolution theorem for the modeling of saturated induction motors. In: IECON 2010–36th Annu Conf IEEE Ind Electron Soc, IEEE; 2010. p. 772–7. es_ES
dc.description.references Ghoggal, A., Sahraoui, M., Zouzou, S. E., & Razik, H. (2013). A Fast Inductance Computation Devoted to the Modeling of Healthy, Eccentric, and Saturated Induction Motors. Electric Power Components and Systems, 41(10), 1002-1022. doi:10.1080/15325008.2013.801056 es_ES
dc.description.references Sapena-Bano, A., Martinez-Roman, J., Puche-Panadero, R., Pineda-Sanchez, M., Perez-Cruz, J., & Riera-Guasp, M. (2018). Induction machine model with space harmonics for fault diagnosis based on the convolution theorem. International Journal of Electrical Power & Energy Systems, 100, 463-481. doi:10.1016/j.ijepes.2018.03.001 es_ES
dc.description.references Horen, Y., Strajnikov, P., & Kuperman, A. (2015). Simple mechanical parameters identification of induction machine using voltage sensor only. Energy Conversion and Management, 92, 60-66. doi:10.1016/j.enconman.2014.12.041 es_ES
dc.description.references Pineda-Sanchez, M., Puche-Panadero, R., Martinez-Roman, J., Sapena-Bano, A., Riera-Guasp, M., & Perez-Cruz, J. (2018). Partial Inductance Model of Induction Machines for Fault Diagnosis. Sensors, 18(7), 2340. doi:10.3390/s18072340 es_ES
dc.description.references Ikeda, M., & Hiyama, T. (2007). Simulation Studies of the Transients of Squirrel-Cage Induction Motors. IEEE Transactions on Energy Conversion, 22(2), 233-239. doi:10.1109/tec.2006.874203 es_ES
dc.description.references Staszak, J. (2013). Determination of slot leakage inductance for three-phase induction motor winding using an analytical method. Archives of Electrical Engineering, 62(4), 569-591. doi:10.2478/aee-2013-0046 es_ES
dc.description.references Joksimovic, G. M., Durovic, M. D., Penman, J., & Arthur, N. (2000). Dynamic simulation of dynamic eccentricity in induction machines-winding function approach. IEEE Transactions on Energy Conversion, 15(2), 143-148. doi:10.1109/60.866991 es_ES
dc.description.references Nandi, S., Bharadwaj, R. M., & Toliyat, H. A. (2002). Performance analysis of a three-phase induction motor under mixed eccentricity condition. IEEE Transactions on Energy Conversion, 17(3), 392-399. doi:10.1109/tec.2002.801995 es_ES
dc.description.references Faiz, J., & Tabatabaei, I. (2002). Extension of winding function theory for nonuniform air gap in electric machinery. IEEE Transactions on Magnetics, 38(6), 3654-3657. doi:10.1109/tmag.2002.804805 es_ES
dc.description.references Bossio, G., DeAngelo, C., Solsona, J., Garcia, G., & Valla, M. I. (2004). A 2-D Model of the Induction Machine: An Extension of the Modified Winding Function Approach. IEEE Transactions on Energy Conversion, 19(1), 144-150. doi:10.1109/tec.2003.822294 es_ES
dc.description.references Faiz, J., Ardekanei, I. T., & Toliyat, H. A. (2003). An evaluation of inductances of a squirrel-cage induction motor under mixed eccentric conditions. IEEE Transactions on Energy Conversion, 18(2), 252-258. doi:10.1109/tec.2003.811740 es_ES
dc.description.references Li, X., Wu, Q., & Nandi, S. (2007). Performance Analysis of a Three-Phase Induction Machine With Inclined Static Eccentricity. IEEE Transactions on Industry Applications, 43(2), 531-541. doi:10.1109/tia.2006.889806 es_ES
dc.description.references Meeker D. Finite element method magnetics. User’s Manual. Version 4.0; 2004. es_ES


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