A general sequential Monte Carlo method based optimal wavelet filter: A Bayesian approach for extracting bearing fault features
A general sequential Monte Carlo method is introduced to optimize a wavelet filter. A state space model of wavelet parameters is built. A joint posterior probability density function of wavelet parameters is derived. The graphical relationship between center frequency and bandwidth is obtained. The approximately optimal center frequency and bandwidth are determined.
monte carlo methods optimization bearing fault diagnosis wavelet transforms vibrations