A New Scheme for Fault Detection and Classification Applied to DC Motor

Autores

  • Laércio I. Santos
  • Reinaldo M. Palhares
  • Marcos F. S. V. D'Angelo Universidade Estadual de Montes Claros Departamento de Ciência da Computação
  • João B. Mendes
  • Renê R. Veloso
  • Petr Y. Ekel

DOI:

https://doi.org/10.5540/tema.2018.019.02.327

Palavras-chave:

Fault Detection and Classification, Luenberger Observer, Particle Swarm Clustering.

Resumo

This study presents an approach for fault detection and classification in a DC drive system. The fault is detected by a classical Luenberger observer. After the fault detection, the fault classification is started. The fault classification, the main contribution of this paper, is based on a representation which combines the Subctrative Clustering algorithm with an adaptation of Particle Swarm Clustering.

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Publicado

2018-09-12

Como Citar

Santos, L. I., Palhares, R. M., D’Angelo, M. F. S. V., Mendes, J. B., Veloso, R. R., & Ekel, P. Y. (2018). A New Scheme for Fault Detection and Classification Applied to DC Motor. Trends in Computational and Applied Mathematics, 19(2), 327. https://doi.org/10.5540/tema.2018.019.02.327

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