Acoustic diagnosis for blower with wavelet transform and neural networks

Author

Kotani, Manabu ; Ueda, Yasuo ; Matsumoto, Haruya ; Kanagawa, Toshihide

Author_Institution

Fac. of Eng., Kobe Univ., Japan

Volume

2

fYear

1995

fDate

Nov/Dec 1995

Firstpage

718

Abstract

It is important for this diagnosis to detect the surging phenomena which lead to the destruction of the blower. Since the surging sound is a non-stationary signal, the wavelet transform is more suitable for the pre-processing method than FFT transform. The dyadic wavelet transform is used as the pre-processing method. The multi-layered neural network is used as the discrimination method. The results show that the neural network with the wavelet transform can detect the surging sound perfectly

Keywords

acoustic signal processing; fault diagnosis; multilayer perceptrons; pattern recognition; wavelet transforms; acoustic diagnosis; blower; discrimination method; dyadic wavelet transform; multi-layered neural network; nonstationary signal; pre-processing method; surging phenomena; Acoustic measurements; Acoustic signal detection; Acoustic waves; Acoustical engineering; Continuous wavelet transforms; Feature extraction; Multi-layer neural network; Neural networks; Pattern recognition; Wavelet transforms;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1995. Proceedings., IEEE International Conference on

Conference_Location

Perth, WA

Print_ISBN

0-7803-2768-3

Type

conf

DOI

10.1109/ICNN.1995.487505

Filename

487505