Open AccessA 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
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.