2026-05-14T04:05:52Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/55752022-11-29T23:42:05Zcom_10259_4219com_10259_5086com_10259_2604col_10259_4220

Juez Gil, Mario 747 500 Saucedo Dorantes, Juan José c72d1ba9-11ec-45c6-be9b-ce38206fb2c1 500 Arnaiz González, Álvar 39 500 0000-0001-6965-0237 López Nozal, Carlos 322 500 0000-0001-8462-212X García Osorio, César 212 500 0000-0002-1206-1084 Lowe, David 2b10979c-7c71-4721-aac0-3482c3dc14af 500 2020-12-10T11:31:26Z 2020-12-10T11:31:26Z 2020-11 0019-0578 http://hdl.handle.net/10259/5575 10.1016/j.isatra.2020.07.002 The detection of faulty machinery and its automated diagnosis is an industrial priority because efficient fault diagnosis implies efficient management of the maintenance times, reduction of energy consumption, reduction in overall costs and, most importantly, the availability of the machinery is ensured. Thus, this paper presents a new intelligent multi-fault diagnosis method based on multiple sensor information for assessing the occurrence of single, combined, and simultaneous faulty conditions in an induction motor. The contribution and novelty of the proposed method include the consideration of different physical magnitudes such as vibrations, stator currents, voltages, and rotational speed as a meaningful source of information of the machine condition. Moreover, for each available physical magnitude, the reduction of the original number of attributes through the Principal Component Analysis leads to retain a reduced number of significant features that allows achieving the final diagnosis outcome by a multi-label classification tree. The effectiveness of the method was validated by using a complete set of experimental data acquired from a laboratory electromechanical system, where a healthy and seven faulty scenarios were assessed. Also, the interpretation of the results do not require any prior expert knowledge and the robustness of this proposal allows its application in industrial applications, since it may deal with different operating conditions such as different loads and operating frequencies. Finally, the performance was evaluated using multi-label measures, which to the best of our knowledge, is an innovative development in the field condition monitoring and fault identification. project TIN2015-67534-P (MINECO, Spain/FEDER, UE) of the Ministerio de Economía y Competitividad of the Spanish Government, project BU085P17 (JCyL/FEDER, UE) of the Consejería de Educación of the Junta de Castilla y León, Spain (both projects co-financed through European Union FEDER funds), and by the pre-doctoral grant (EDU/1100/2017), also of the Consejería de Educación of the Junta de Castilla y León, Spain and the European Social Fund. application/pdf eng Elsevier ISA Transactions. 2020, V. 106, p.367-381 https://doi.org/10.1016/j.isatra.2020.07.002 info:eu-repo/grantAgreement/MINECO/TIN2015-67534-P info:eu-repo/grantAgreement/JCyL/BU085P17 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess Multi-fault detection Early detection Multi-label classification Principal component analysis Load insensitive model Prediction at low operating frequencies Informática Computer science Early and extremely early multi-label fault diagnosis in induction motors info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion ISA Transactions 106 367 381 TEXT Juez-it-2020.pdf.txt Juez-it-2020.pdf.txt Extracted text text/plain 87669 https://riubu.ubu.es/bitstream/10259/5575/5/Juez-it-2020.pdf.txt 64520472a2a1c8fc9f7bc38a0c7632da MD5 5 THUMBNAIL Juez-it-2020.pdf.jpg Juez-it-2020.pdf.jpg IM Thumbnail image/jpeg 3825 https://riubu.ubu.es/bitstream/10259/5575/4/Juez-it-2020.pdf.jpg a35c20b9b0cf62ea0c89088966ce3f2b MD5 4 LICENSE license.txt license.txt text/plain; charset=utf-8 999 https://riubu.ubu.es/bitstream/10259/5575/3/license.txt b295bcbce42e2caabeb0c623d3860c06 MD5 3 CC-LICENSE license_rdf license_rdf application/rdf+xml; charset=utf-8 805 https://riubu.ubu.es/bitstream/10259/5575/2/license_rdf 4460e5956bc1d1639be9ae6146a50347 MD5 2 ORIGINAL Juez-it-2020.pdf Juez-it-2020.pdf application/pdf 4082741 https://riubu.ubu.es/bitstream/10259/5575/1/Juez-it-2020.pdf 911dc7f4159bfaab729b6638195ee6c6 MD5 1 10259/5575 oai:riubu.ubu.es:10259/5575 2022-11-30 00:42:05.745 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es 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