Title :
Fault diagnosis of rotating machinery based on wavelet transforms and neural network
Author :
Roztocil, Jan ; Novak, Martin
Author_Institution :
Dept. of Instrum. & Control Eng., Czech Tech. Univ. in Prague, Prague, Czech Republic
Abstract :
This paper shows usage of wavelet transform for condition evaluation of the rotating machinery by processing a signal of instantaneous angular velocity. One or more machine revolutions are used for the state evaluation. Wavelet transformation is applied to form a feature vector which, transformed by a neural network into a fault vector, is used for the description of a rotating machinery condition. Results obtained with a 2 cylinder four-stroke piston diesel engine ČKD S110 are shown.
Keywords :
condition monitoring; diesel engines; fault diagnosis; maintenance engineering; mechanical engineering computing; neural nets; signal processing; wavelet transforms; CKD S110 engine; condition evaluation; fault diagnosis; four-stroke piston diesel engine; instantaneous angular velocity; neural network; rotating machinery; state evaluation; wavelet transform; Angular velocity; Artificial neural networks; Engines; Pistons; Support vector machine classification; Wavelet transforms;
Conference_Titel :
Applied Electronics (AE), 2010 International Conference on
Conference_Location :
Pilsen
Print_ISBN :
978-80-7043-865-7
