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

Laércio Ives Santos | Zendy; Reinaldo M. Palhares | Zendy; Marcos Flávio Silveira Vasconcelos D’Ângelo | Zendy; João Batista Mendes | Zendy; Renê Rodrigues Veloso | Zendy; Petr Ekel | Zendy

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

Author(s) -

Laércio Ives Santos,

Reinaldo M. Palhares,

Marcos Flávio Silveira Vasconcelos D’Ângelo,

João Batista Mendes,

Renê Rodrigues Veloso,

Petr Ekel

Publication year - 2018

Publication title -

tema (são carlos)

Language(s) - English

Resource type - Journals

eISSN - 2179-8451

pISSN - 1677-1966

DOI - 10.5540/tema.2018.019.02.327

Subject(s) - cluster analysis , fault detection and isolation , fault (geology) , observer (physics) , computer science , representation (politics) , control theory (sociology) , scheme (mathematics) , pattern recognition (psychology) , dc motor , classification scheme , artificial intelligence , algorithm , machine learning , mathematics , engineering , actuator , physics , control (management) , mathematical analysis , electrical engineering , quantum mechanics , seismology , politics , political science , law , geology

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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