Publication
Stator winding fault detection using external search coil and artificial neural network
| dc.contributor.author | Vicente, João | |
| dc.contributor.author | Ferreira, Ângela P. | |
| dc.contributor.author | Castoldi, Marcelo | |
| dc.contributor.author | Teixeira, João Paulo | |
| dc.contributor.author | Goedtel, Alessandro | |
| dc.date.accessioned | 2022-01-12T15:41:01Z | |
| dc.date.available | 2022-01-12T15:41:01Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | This paper presents a methodology for winding stator fault detection of induction motors, using an external search coil, which is a noninvasive technique and can be applied during motor operation. The dispersion magnetic flux of the motor operating in abnormal conditions induces a voltage in the search coil that differs from a reference pattern corresponding to the healthy stator winding. Experimental data were obtained in a test bench using a 0.75 kW three-phase squirrel-cage induction motor with the stator winding modified to allow the introduction of short circuits. This work considered short circuits in one phase, involving 1%, 3%, 5% and 10% of the turns, with the motor loaded with a varying torque. Fault diagnosis is obtained through two models of artificial neural networks, implemented with the signals in the time domain. The obtained results demonstrated that the developed methodology presents difficulties in predicting short circuits in incipient stages, but for short circuits of higher severity, the behaviour improved substantially, being 100% successful for faults with 10% turns short-circuited. | pt_PT |
| dc.description.sponsorship | This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UIDB/05757/2020. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Vicente, João: Ferreira, Ângela P.; Castoldi, Marcelo; Teixeira, João Paulo; Goedtel, Alessandro (2020). Stator winding fault detection using external search coil and artificial neural network. In 9th Scientific-Technical Conference on E-mobility, Sustainable Materials and Technologies (MATBUD). Cracow. p. 1-9. ISBN 978-2-7598-9108-5 | pt_PT |
| dc.identifier.doi | 10.1051/matecconf/202032201054 | pt_PT |
| dc.identifier.isbn | 978-2-7598-9108-5 | |
| dc.identifier.uri | http://hdl.handle.net/10198/24592 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.relation | Research Centre in Digitalization and Intelligent Robotics | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Induction | pt_PT |
| dc.subject | Motor diagnosis | pt_PT |
| dc.title | Stator winding fault detection using external search coil and artificial neural network | pt_PT |
| dc.type | conference paper | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | Research Centre in Digitalization and Intelligent Robotics | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT | |
| oaire.citation.startPage | 01054 | pt_PT |
| oaire.citation.title | MATEC Web of Conferences | pt_PT |
| oaire.citation.volume | 322 | pt_PT |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| person.familyName | Ferreira | |
| person.familyName | Teixeira | |
| person.givenName | Ângela P. | |
| person.givenName | João Paulo | |
| person.identifier | 663194 | |
| person.identifier.ciencia-id | 2211-6787-D936 | |
| person.identifier.ciencia-id | 4F15-B322-59B4 | |
| person.identifier.orcid | 0000-0002-1912-2556 | |
| person.identifier.orcid | 0000-0002-6679-5702 | |
| person.identifier.rid | M-8188-2013 | |
| person.identifier.rid | N-6576-2013 | |
| person.identifier.scopus-author-id | 55516840300 | |
| person.identifier.scopus-author-id | 57069567500 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | conferenceObject | pt_PT |
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