Title :
Applying neural networks to determine vibration parameters in a turbine
Author :
Caulkins, C.W. ; Oliveira, R.B.T. ; Carvalho, A.C.P.L.F. ; Rezende, S.O. ; Monard, M.C.
Author_Institution :
Dept. of Comput. Sci., Sao Paulo Univ., Brazil
Abstract :
Vibration signals analysis is considered as an appropriate diagnosis method for detecting faults. Several techniques have been used for detecting vibration signals. In this work, artificial neural networks (ANN) were used to predict vibration signals using process parameters measured in a turbine. The ANN models can be viewed as “black-boxes”. One way to improve their comprehensibility is to use a symbolic model. As a first step in this direction, a hybrid rule-based regression model was also tested
Keywords :
condition monitoring; diagnostic expert systems; fault diagnosis; multilayer perceptrons; radial basis function networks; turbines; fault diagnosis; multilayer perceptrons; neural networks; radial basis function neural nets; rule-based regression model; symbolic model; turbine; vibration signals analysis; Artificial neural networks; Fault detection; Fault diagnosis; Neural networks; Signal analysis; Signal detection; Signal processing; Testing; Turbines; Vibration measurement;
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5529-6
DOI :
10.1109/IJCNN.1999.836203