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Detection of adjacent and non-adjacent bar breakages in induction motors via convolutional analysis of sound signals

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Detection of adjacent and non-adjacent bar breakages in induction motors via convolutional analysis of sound signals

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dc.contributor.author Iglesias Martínez, Miguel Enrique es_ES
dc.contributor.author Fernández de Córdoba, Pedro es_ES
dc.contributor.author Antonino Daviu, José Alfonso es_ES
dc.contributor.author Conejero, J. Alberto es_ES
dc.date.accessioned 2021-05-12T03:31:51Z
dc.date.available 2021-05-12T03:31:51Z
dc.date.issued 2020-10 es_ES
dc.identifier.uri http://hdl.handle.net/10251/166204
dc.description.abstract [EN] We apply power spectral analysis based on covariance function and spectral subtraction to detect adjacent and non-adjacent bar breakages. We get a spectral pattern when the signal presents one or various broken bars, independent of the relative position of the bar breakages. The proposed algorithm gives satisfactory results about detectability compared to some previous researches. Additionally, we also present illustrations of faults and signal to noise in the noise reduction stage. es_ES
dc.description.sponsorship This research was funded by MEC, grant number MTM 2016-7963-P; Spanish `Ministerio de Ciencia Innovacion y Universidades' and FEDER program in the framework of the `Proyectos de I +D de Generacion de Conocimiento del Programa Estatal de Generacion de Conocimiento y Fortalecimiento Cientifico y Tecnologico del Sistema de I+D+i, Subprograma Estatal de Generacion de Conocimiento' (ref: PGC2018-095747-B-I00); and Generalitat Valenciana, Conselleria de Innovacion, Universidades, Ciencia y Sociedad Digital, (project AICO/019/224). es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Applied Sciences es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Electrical machines es_ES
dc.subject Rotor bar breakages es_ES
dc.subject Spectral analysis es_ES
dc.subject Noise es_ES
dc.subject.classification MATEMATICA APLICADA es_ES
dc.subject.classification INGENIERIA ELECTRICA es_ES
dc.title Detection of adjacent and non-adjacent bar breakages in induction motors via convolutional analysis of sound signals es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/app10196641 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//MTM2016-75963-P/ES/DINAMICA DE OPERADORES/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PGC2018-095747-B-I00/ES/TECNOLOGIAS AVANZADAS BASADAS EN EL ANALISIS DEL FLUJO DE DISPERSION EN REGIMEN TRANSITORIO PARA EL DIAGNOSTICO PRECOZ DE ANOMALIAS ELECTROMECANICAS EN MOTORES ELECTRICOS/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GVA//AICO%2F2019%2F224/ 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.; Fernández De Córdoba, P.; Antonino Daviu, JA.; Conejero, JA. (2020). Detection of adjacent and non-adjacent bar breakages in induction motors via convolutional analysis of sound signals. Applied Sciences. 10(19):1-19. https://doi.org/10.3390/app10196641 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/app10196641 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 10 es_ES
dc.description.issue 19 es_ES
dc.identifier.eissn 2076-3417 es_ES
dc.relation.pasarela S\417116 es_ES
dc.contributor.funder Generalitat Valenciana es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
dc.description.references Cusidó, J., Romeral, L., Ortega, J. A., Garcia, A., & Riba, J. (2011). Signal Injection as a Fault Detection Technique. Sensors, 11(3), 3356-3380. doi:10.3390/s110303356 es_ES
dc.description.references Riera-Guasp, M., Cabanas, M. F., Antonino-Daviu, J. A., Pineda-Sanchez, M., & Garcia, C. H. R. (2010). Influence of Nonconsecutive Bar Breakages in Motor Current Signature Analysis for the Diagnosis of Rotor Faults in Induction Motors. IEEE Transactions on Energy Conversion, 25(1), 80-89. doi:10.1109/tec.2009.2032622 es_ES
dc.description.references Garcia-Perez, A., Romero-Troncoso, R. J., Cabal-Yepez, E., Osornio-Rios, R. A., & Lucio-Martinez, J. A. (2011). Application of high-resolution spectral analysis for identifying faults in induction motors by means of sound. Journal of Vibration and Control, 18(11), 1585-1594. doi:10.1177/1077546311422925 es_ES
dc.description.references Glowacz, A., Glowacz, W., Glowacz, Z., Kozik, J., Gutten, M., Korenciak, D., … Carletti, E. (2017). Fault Diagnosis of Three Phase Induction Motor Using Current Signal, MSAF-Ratio15 and Selected Classifiers. Archives of Metallurgy and Materials, 62(4), 2413-2419. doi:10.1515/amm-2017-0355 es_ES
dc.description.references Guezmil, A., Berriri, H., Pusca, R., Sakly, A., Romary, R., & Mimouni, M. F. (2017). Detecting Inter-Turn Short-Circuit Fault in Induction Machine Using High-Order Sliding Mode Observer: Simulation and Experimental Verification. Journal of Control, Automation and Electrical Systems, 28(4), 532-540. doi:10.1007/s40313-017-0314-2 es_ES
dc.description.references Zhong, J.-H., Wong, P., & Yang, Z.-X. (2016). Simultaneous-Fault Diagnosis of Gearboxes Using Probabilistic Committee Machine. Sensors, 16(2), 185. doi:10.3390/s16020185 es_ES
dc.description.references Iglesias-Martínez, M., Antonino-Daviu, J., Fernández de Córdoba, P., & Conejero, J. (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), 597. doi:10.3390/en12040597 es_ES
dc.description.references Glowacz, A., Glowacz, W., Glowacz, Z., & Kozik, J. (2018). Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals. Measurement, 113, 1-9. doi:10.1016/j.measurement.2017.08.036 es_ES
dc.description.references Samanta, A. K., Naha, A., Routray, A., & Deb, A. K. (2018). Fast and accurate spectral estimation for online detection of partial broken bar in induction motors. Mechanical Systems and Signal Processing, 98, 63-77. doi:10.1016/j.ymssp.2017.04.035 es_ES
dc.description.references Antonino-Daviu, J., Riera-Guasp, M., Roger-Folch, J., Martínez-Giménez, F., & Peris, A. (2006). Application and optimization of the discrete wavelet transform for the detection of broken rotor bars in induction machines. Applied and Computational Harmonic Analysis, 21(2), 268-279. doi:10.1016/j.acha.2005.12.003 es_ES
dc.description.references Bazhenov, V. A., Pogorelova, O. S., & Postnikova, T. G. (2018). Intermittent transition to chaos in vibroimpact system. Applied Mathematics and Nonlinear Sciences, 3(2), 475-486. doi:10.2478/amns.2018.2.00037 es_ES
dc.description.references Gaeid, K. S., Ping, H. W., Khalid, M., & Masaoud, A. (2012). Sensor and Sensorless Fault Tolerant Control for Induction Motors Using a Wavelet Index. Sensors, 12(4), 4031-4050. doi:10.3390/s120404031 es_ES
dc.description.references Yahia, K., Cardoso, A. J. M., Ghoggal, A., & Zouzou, S. E. (2014). Induction motors airgap-eccentricity detection through the discrete wavelet transform of the apparent power signal under non-stationary operating conditions. ISA Transactions, 53(2), 603-611. doi:10.1016/j.isatra.2013.12.002 es_ES
dc.description.references Delgado-Arredondo, P. A., Morinigo-Sotelo, D., Osornio-Rios, R. A., Avina-Cervantes, J. G., Rostro-Gonzalez, H., & Romero-Troncoso, R. de J. (2017). Methodology for fault detection in induction motors via sound and vibration signals. Mechanical Systems and Signal Processing, 83, 568-589. doi:10.1016/j.ymssp.2016.06.032 es_ES
dc.description.references Gu, F., Wang, T., Alwodai, A., Tian, X., Shao, Y., & Ball, A. D. (2015). A new method of accurate broken rotor bar diagnosis based on modulation signal bispectrum analysis of motor current signals. Mechanical Systems and Signal Processing, 50-51, 400-413. doi:10.1016/j.ymssp.2014.05.017 es_ES
dc.description.references Saidi, L., Fnaiech, F., Henao, H., Capolino, G.-A., & Cirrincione, G. (2013). Diagnosis of broken-bars fault in induction machines using higher order spectral analysis. ISA Transactions, 52(1), 140-148. doi:10.1016/j.isatra.2012.08.003 es_ES
dc.description.references Głowacz, A., & Głowacz, Z. (2017). Recognition of rotor damages in a DC motor using acoustic signals. Bulletin of the Polish Academy of Sciences Technical Sciences, 65(2), 187-194. doi:10.1515/bpasts-2017-0023 es_ES
dc.description.references Ondel, O., Boutleux, E., & Clerc, G. (2006). A method to detect broken bars in induction machine using pattern recognition techniques. IEEE Transactions on Industry Applications, 42(4), 916-923. doi:10.1109/tia.2006.876071 es_ES
dc.description.references Júnior, A. M. G., Silva, V. V. R., Baccarini, L. M. R., & Mendes, L. F. S. (2018). The design of multiple linear regression models using a genetic algorithm to diagnose initial short-circuit faults in 3-phase induction motors. Applied Soft Computing, 63, 50-58. doi:10.1016/j.asoc.2017.11.015 es_ES
dc.description.references Rezazadeh Mehrjou, M., Mariun, N., Misron, N., Radzi, M., & Musa, S. (2017). Broken Rotor Bar Detection in LS-PMSM Based on Startup Current Analysis Using Wavelet Entropy Features. Applied Sciences, 7(8), 845. doi:10.3390/app7080845 es_ES
dc.description.references Iglesias-Martinez, M. E., Fernandez de Cordoba, P., Antonino-Daviu, J. A., & Conejero, J. A. (2019). Detection of Nonadjacent Rotor Faults in Induction Motors via Spectral Subtraction and Autocorrelation of Stray Flux Signals. IEEE Transactions on Industry Applications, 55(5), 4585-4594. doi:10.1109/tia.2019.2917861 es_ES
dc.description.references Iglesias Martínez, M. E., Antonino-Daviu, J. A., de Córdoba, P. F., & Conejero, J. A. (2020). Higher-Order Spectral Analysis of Stray Flux Signals for Faults Detection in Induction Motors. Applied Mathematics and Nonlinear Sciences, 5(2), 1-14. doi:10.2478/amns.2020.1.00032 es_ES
dc.description.references Dhabu, S., Ambede, A., Agrawal, N., Smitha, K. G., Darak, S., & Vinod, A. P. (2020). Variable cutoff frequency FIR filters: a survey. SN Applied Sciences, 2(3). doi:10.1007/s42452-020-2140-6 es_ES
dc.description.references Isogawa, K., Ida, T., Shiodera, T., & Takeguchi, T. (2018). Deep Shrinkage Convolutional Neural Network for Adaptive Noise Reduction. IEEE Signal Processing Letters, 25(2), 224-228. doi:10.1109/lsp.2017.2782270 es_ES
dc.description.references Crouse, M. S., Nowak, R. D., & Baraniuk, R. G. (1998). Wavelet-based statistical signal processing using hidden Markov models. IEEE Transactions on Signal Processing, 46(4), 886-902. doi:10.1109/78.668544 es_ES
dc.description.references Ge, H., Chen, G., Yu, H., Chen, H., & An, F. (2018). Theoretical Analysis of Empirical Mode Decomposition. Symmetry, 10(11), 623. doi:10.3390/sym10110623 es_ES
dc.subject.ods 09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación es_ES


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