Acoustic diagnosis for blower with wavelet transform and neural networks
Manabu Kotani, Y. Ueda, H. Matsumoto, Toshihide KANAGAWA · 2002
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.