Title :
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
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;
Conference_Titel :
Neural Networks, 1995. Proceedings., IEEE International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-2768-3
DOI :
10.1109/ICNN.1995.487505
