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

fYear

2010

fDate

8-9 Sept. 2010

Firstpage

1

Lastpage

6

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Applied Electronics (AE), 2010 International Conference on

Conference_Location

Pilsen

ISSN

1803-7232

Print_ISBN

978-80-7043-865-7

Type

conf

Filename

5599565