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dc.contributor.author | Iglesias-Martínez, Miguel E. | es_ES |
dc.contributor.author | Antonino Daviu, José Alfonso | es_ES |
dc.contributor.author | Fernández de Córdoba, Pedro | es_ES |
dc.contributor.author | Conejero, J. Alberto | es_ES |
dc.date.accessioned | 2020-12-19T04:32:09Z | |
dc.date.available | 2020-12-19T04:32:09Z | |
dc.date.issued | 2019-02-02 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/157507 | |
dc.description.abstract | [EN] The aim of this work is to find out, through the analysis of the time and frequency domains, significant differences that lead us to obtain one or several variables that may result in an indicator that allows diagnosing the condition of the rotor in an induction motor from the processing of the stray flux signals. For this, the calculation of two indicators is proposed: the first is based on the frequency domain and it relies on the calculation of the sum of the mean value of the bispectrum of the flux signal. The use of high order spectral analysis is justified in that with the one-dimensional analysis resulting from the Fourier Transform, there may not always be solid differences at the spectral level that enable us to distinguish between healthy and faulty conditions. Also, based on the high-order spectral analysis, differences may arise that, with the classical analysis with the Fourier Transform, are not evident, since the high order spectra from the Bispectrum are immune to Gaussian noise, but not the results that can be obtained using the one-dimensional Fourier transform. On the other hand, a second indicator based on the temporal domain that is based on the calculation of the square value of the median of the autocovariance function of the signal is evaluated. The obtained results are satisfactory and let us conclude the affirmative hypothesis of using flux signals for determining the condition of the rotor of an induction motor. | es_ES |
dc.description.sponsorship | This research was funded by MEC, grant number MTM 2016-7963-P. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | MDPI AG | es_ES |
dc.relation.ispartof | Energies | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | Indicator | es_ES |
dc.subject | Fault diagnosis | es_ES |
dc.subject | Induction motors | es_ES |
dc.subject | Bispectrum | es_ES |
dc.subject | Autocovariance | es_ES |
dc.subject.classification | MATEMATICA APLICADA | es_ES |
dc.subject.classification | INGENIERIA ELECTRICA | es_ES |
dc.title | Rotor fault detection in induction motors based on time-frequency analysis using the bispectrum and the autocovariance of stray flux signals | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.3390/en12040597 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MINECO//MTM2016-75963-P/ES/DINAMICA DE OPERADORES/ | 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.contributor.affiliation | Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada | es_ES |
dc.description.bibliographicCitation | Iglesias-Martínez, ME.; Antonino Daviu, JA.; Fernández De Córdoba, P.; Conejero, JA. (2019). Rotor fault detection in induction motors based on time-frequency analysis using the bispectrum and the autocovariance of stray flux signals. Energies. 12(4):1-16. https://doi.org/10.3390/en12040597 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.3390/en12040597 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 16 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 12 | es_ES |
dc.description.issue | 4 | es_ES |
dc.identifier.eissn | 1996-1073 | es_ES |
dc.relation.pasarela | S\378072 | es_ES |
dc.contributor.funder | Ministerio de Economía y Competitividad | es_ES |
dc.description.references | Henao, H., Capolino, G.-A., Fernandez-Cabanas, M., Filippetti, F., Bruzzese, C., Strangas, E., … Hedayati-Kia, S. (2014). Trends in Fault Diagnosis for Electrical Machines: A Review of Diagnostic Techniques. IEEE Industrial Electronics Magazine, 8(2), 31-42. doi:10.1109/mie.2013.2287651 | es_ES |
dc.description.references | Riera-Guasp, M., Antonino-Daviu, J. A., & Capolino, G.-A. (2015). Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art. IEEE Transactions on Industrial Electronics, 62(3), 1746-1759. doi:10.1109/tie.2014.2375853 | es_ES |
dc.description.references | Jiang, C., Li, S., & Habetler, T. G. (2017). A review of condition monitoring of induction motors based on stray flux. 2017 IEEE Energy Conversion Congress and Exposition (ECCE). doi:10.1109/ecce.2017.8096907 | es_ES |
dc.description.references | Ramirez-Nunez, J. A., Antonino-Daviu, J. A., Climente-Alarcon, V., Quijano-Lopez, A., Razik, H., Osornio-Rios, R. A., & Romero-Troncoso, R. de J. (2018). Evaluation of the Detectability of Electromechanical Faults in Induction Motors Via Transient Analysis of the Stray Flux. IEEE Transactions on Industry Applications, 54(5), 4324-4332. doi:10.1109/tia.2018.2843371 | es_ES |
dc.description.references | Park, Y., Yang, C., Kim, J., Kim, H., Lee, S. B., Gyftakis, K. N., … Capolino, G.-A. (2019). Stray Flux Monitoring for Reliable Detection of Rotor Faults Under the Influence of Rotor Axial Air Ducts. IEEE Transactions on Industrial Electronics, 66(10), 7561-7570. doi:10.1109/tie.2018.2880670 | es_ES |
dc.description.references | Iglesias-Martinez, M. E., de Cordoba, P. F., Antonino-Daviu, J. A., & Conejero, J. A. (2018). Detection of Bar Breakages in Induction Motor via Spectral Subtraction of Stray Flux Signals. 2018 XIII International Conference on Electrical Machines (ICEM). doi:10.1109/icelmach.2018.8507078 | es_ES |
dc.description.references | Mendel, J. M. (1991). Tutorial on higher-order statistics (spectra) in signal processing and system theory: theoretical results and some applications. Proceedings of the IEEE, 79(3), 278-305. doi:10.1109/5.75086 | es_ES |
dc.description.references | Nikias, C. L., & Mendel, J. M. (1993). Signal processing with higher-order spectra. IEEE Signal Processing Magazine, 10(3), 10-37. doi:10.1109/79.221324 | es_ES |
dc.subject.ods | 09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación | es_ES |