Title :
On fault classification in rotating machines using fourier domain features and neural networks
Author :
de Lima, Amaro A. ; de M Prego, Thiago ; Netto, Sergio L. ; da Silva, E.A.B. ; Gutierrez, R.H.R. ; Monteiro, U.A. ; Troyman, A.C.R. ; da C Silveira, Francisco J. ; Vaz, L.
Author_Institution :
Fed. Center of Tech. Educ. Celso Suckow da Fonseca (CEFET-RJ) - Nova Iguacu, Nova Iguacu, Brazil
fDate :
Feb. 27 2013-March 1 2013
Abstract :
The paper addresses the problem of classifying mechanical faults in rotating machines. In this context, three operational classes are considered, namely: normal (where the machine has no fault), unbalance (where the machine load has its weight not equally distributed), and misalignment (where the rotor and machine axes are dislocated from its natural concentric position). A large dataset consisting of 606 distinct scenarios is developed for system training and testing, along with a preprocessing strategy that improves data distribution among the three classes considered. A classifier based on an artificial neural network is described, achieving a global accuracy rate of 93.5%.
Keywords :
Fourier analysis; fault diagnosis; feature extraction; machinery; mechanical engineering computing; mechanical testing; neural nets; pattern classification; rotors; Fourier domain features; artificial neural network; data distribution; machine axes; machine misalignment; mechanical fault classification; natural concentric position; neural networks; normal load; rotating machines; system testing; system training; unbalance machine load; Artificial neural networks; Circuit faults; Databases; Frequency estimation; Radio frequency; Training;
Conference_Titel :
Circuits and Systems (LASCAS), 2013 IEEE Fourth Latin American Symposium on
Conference_Location :
Cusco
Print_ISBN :
978-1-4673-4897-3
DOI :
10.1109/LASCAS.2013.6518984
