Automated classification of tribological faults of alternative systems with the use of unsupervised artificial neural networks

Automated classification of tribological faults of alternative systems with the use of unsupervised artificial neural networks

M.A.L. Cabral, E.P. Matamoros, J.A.F. Costa, A.P.V. Pinto, A. Silva de Souza, A.D. Freire, C.E.F. Bezerra, E.L. dos Santos Cabral, W.R. Silva Castro, R.P. de Souza, E.A.R. Seabra

ARTIGO

Inglês

Agradecimentos: This research was supported by UFRN – Universidade Federal do Rio Grande do Norte, Natal, RN, Brazil, which is highly appreciated by the authors

Preventing, anticipating, avoiding failures in electromechanical systems are demands that have challenged researchers and engineering professionals for decades. Electromechanical systems present tribological processes that result in fatigue of materials and consequent loss of efficiency or even...

Fechado

Automated classification of tribological faults of alternative systems with the use of unsupervised artificial neural networks

M.A.L. Cabral, E.P. Matamoros, J.A.F. Costa, A.P.V. Pinto, A. Silva de Souza, A.D. Freire, C.E.F. Bezerra, E.L. dos Santos Cabral, W.R. Silva Castro, R.P. de Souza, E.A.R. Seabra


										

Automated classification of tribological faults of alternative systems with the use of unsupervised artificial neural networks

M.A.L. Cabral, E.P. Matamoros, J.A.F. Costa, A.P.V. Pinto, A. Silva de Souza, A.D. Freire, C.E.F. Bezerra, E.L. dos Santos Cabral, W.R. Silva Castro, R.P. de Souza, E.A.R. Seabra

    Fontes

    Journal of computational and theoretical nanoscience

    Vol. 16, no. 7 (July, 2019), p. 2644-2659