Monitoring the stator current in induction machines for possible fault detection: a fuzzy/bayesian approach for the problem of time series multiple change point detection
| dc.creator | Marcos Flávio Silveira Vasconcelos D'Angelo | |
| dc.creator | Reinaldo Martinez Palhares | |
| dc.creator | Renato Dourado Maia | |
| dc.creator | João Batista Mendes | |
| dc.creator | Petr Iakovlevitch Ekel | |
| dc.creator | Camila Katheryne Santos Cangussu | |
| dc.creator | Lucas Almeida Aguiar | |
| dc.date.accessioned | 2022-07-04T14:39:23Z | |
| dc.date.accessioned | 2025-09-09T01:00:18Z | |
| dc.date.available | 2022-07-04T14:39:23Z | |
| dc.date.issued | 2016 | |
| dc.description.sponsorship | CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico | |
| dc.description.sponsorship | FAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas Gerais | |
| dc.identifier.doi | https://doi.org/10.1590/0101-7438.2016.036.02.0301 | |
| dc.identifier.issn | 1678-5142 | |
| dc.identifier.uri | https://hdl.handle.net/1843/42875 | |
| dc.language | eng | |
| dc.publisher | Universidade Federal de Minas Gerais | |
| dc.relation.ispartof | Pesquisa Operacional | |
| dc.rights | Acesso Aberto | |
| dc.subject | Engenharia de produção | |
| dc.subject | Inteligência computacional | |
| dc.subject | Inteligência artificial | |
| dc.subject | Estator | |
| dc.subject | Máquinas elétricas de indução | |
| dc.subject.other | Abordagem Fuzzy/Bayesiana | |
| dc.subject.other | Classificação de falhas | |
| dc.subject.other | Diagnóstico de Falhas | |
| dc.subject.other | Detecção de Falhas | |
| dc.subject.other | Inteligência Computacional | |
| dc.subject.other | Inteligência Artificial | |
| dc.title | Monitoring the stator current in induction machines for possible fault detection: a fuzzy/bayesian approach for the problem of time series multiple change point detection | |
| dc.type | Artigo de periódico | |
| local.citation.epage | 320 | |
| local.citation.issue | 2 | |
| local.citation.spage | 301 | |
| local.citation.volume | 36 | |
| local.description.resumo | This paper addresses the problem of fault detection in stator winding of induction machine by a multiple change points detection approach in time series. To handle this problem a new fuzzy/Bayesian approach is proposed which differs from previous approaches since it does not require prior information as: the number of change points or the characterization of the data probabilistic distribution. The approach has been applied in the monitoring the current of the stator winding induction machine. The good results obtained by proposed methodology illustrate its efficiency. | |
| local.publisher.country | Brasil | |
| local.publisher.department | ICA - INSTITUTO DE CIÊNCIAS AGRÁRIAS | |
| local.publisher.initials | UFMG | |
| local.url.externa | https://www.scielo.br/j/pope/a/qHsr8M4mC3CnzmfqQwcJRMB/abstract/?lang=en |