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dc.contributor.author | Saucedo-Dorantes, Juan Jose | es_ES |
dc.contributor.author | Zamudio-Ramirez, Israel | es_ES |
dc.contributor.author | Cureño-Osornio, Jonathan | es_ES |
dc.contributor.author | Osornio-Rios, Roque Alfredo | es_ES |
dc.contributor.author | Antonino-Daviu, J. | es_ES |
dc.date.accessioned | 2023-07-11T18:01:32Z | |
dc.date.available | 2023-07-11T18:01:32Z | |
dc.date.issued | 2021-08-30 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/194827 | |
dc.description.abstract | [EN] Bearings are the elements that allow the rotatory movement in induction motors and the fault occurrence in these elements are due to excessive working conditions. In induction motors, elec-trical erosion remains the most common phenomenon that damages bearings, leading to incipient faults that gradually increase to irreparable damages. Thus, condition monitoring strategies ca-pable of assessing bearing fault severities are mandatory to overcome this critical issue. The contribution of this work lies in the proposal of a condition monitoring strategy that is focused on the analysis and identification of different fault severities of the outer race bearing fault in an induction motor. The proposed approach is supported by fusion information of different physical magnitudes and the use of Machine Learning and Artificial Intelligence. An important aspect of this proposal is the calculation of a hybrid-set of statistical features that are obtained to characterize vibration and stator current signals by its processing through domain analysis, i.e., time-domain and frequency-domain; also, the fusion of information of both signals by means of the Linear Discriminant Analysis is important due to the most discriminative and meaningful information is retained resulting in a high-performance condition characterization. Besides, a Neural Net-work-based classifier allows validating the effectiveness of fusion information from different physical magnitudes to face the diagnosis of multiple fault severities that appear in the bearing outer race. The method is validated under an experimental data set that includes information re-lated to a healthy condition and five different severities that appear in the outer race of bearings. | es_ES |
dc.description.sponsorship | This work was supported by the Spanish `Ministerio de Ciencia Innovación y Universidades and FEDER program in the framework of the `Proyectos de I+D de Generación de Conocimiento del Programa Estatal de Generación de Conocimiento y Fortalecimiento Científico y Tecnológico del Sistema de I+D+i, Subprograma Estatal de Generación de Conocimiento (ref: PGC2018-095747-B-I00), and Consejo Nacional de Ciencia y Tecnología (CONACyT) under scholarship 652815. | 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 | Condition Monitoring | es_ES |
dc.subject | Fault Severity | es_ES |
dc.subject | Bearings | es_ES |
dc.subject | Feature Calculation | es_ES |
dc.subject | Feature Extraction | es_ES |
dc.subject | Information Fusion | es_ES |
dc.subject | Neural-Network | es_ES |
dc.subject | Linear Discriminant Analysis. | es_ES |
dc.subject.classification | INGENIERIA ELECTRICA | es_ES |
dc.title | Condition Monitoring Method for the Detection of Fault Graduality in Outer Race Bearing Based on Vibration-Current Fusion, Statistical Features and Neural Network | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.3390/app11178033 | 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/CONACYT//652815/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros Industriales - Escola Tècnica Superior d'Enginyers Industrials | es_ES |
dc.description.bibliographicCitation | Saucedo-Dorantes, JJ.; Zamudio-Ramirez, I.; Cureño-Osornio, J.; Osornio-Rios, RA.; Antonino-Daviu, J. (2021). Condition Monitoring Method for the Detection of Fault Graduality in Outer Race Bearing Based on Vibration-Current Fusion, Statistical Features and Neural Network. Applied Sciences. 11(17):1-20. https://doi.org/10.3390/app11178033 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.3390/app11178033 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 20 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 11 | es_ES |
dc.description.issue | 17 | es_ES |
dc.identifier.eissn | 2076-3417 | es_ES |
dc.relation.pasarela | S\444589 | es_ES |
dc.contributor.funder | AGENCIA ESTATAL DE INVESTIGACION | es_ES |
dc.contributor.funder | European Regional Development Fund | es_ES |
dc.contributor.funder | Consejo Nacional de Ciencia y Tecnología, México | es_ES |