| Texto completo | |
| Autor(es): |
Número total de Autores: 3
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| Afiliação do(s) autor(es): | [1] SEMEQ. Limeria
[2] SEMEQ. Limeria
[3] Univ Estadual Campinas. Comp Inst
Número total de Afiliações: 3
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| Tipo de documento: | Artigo Científico |
| Fonte: | APPLIED ARTIFICIAL INTELLIGENCE; v. 27, n. 1, p. 36-49, 2013. |
| Citações Web of Science: | 1 |
| Resumo | |
This article presents a combination of support vector machine (SVM) and k-nearest neighbor (k-NN) to monitor rotational machines using vibrational data. The system is used as triage for human analysis and, thus, a very low false negative rate is more important than high accuracy. Data are classified using a standard SVM, but for data within the SVM margin, where misclassifications are more like, a k-NN is used to reduce the false negative rate. Using data from a month of operations of a predictive maintenance company, the system achieved a zero false negative rate and accuracy ranging from 75% to 84% for different machine types such as induction motors, gears, and rolling-element bearings. (AU) | |
| Processo FAPESP: | 07/57995-3 - Verificacao de workflow. |
| Beneficiário: | Amaury Bosso André |
| Modalidade de apoio: | Bolsas no Brasil - Mestrado |