Adaptive wavelet filter based on Fractional Lower Order Moment for bearing fault diagnosis

Wavelet analysis has been widely used in signal de-noising or transient signal detection due to its extraordinary time-frequency representation capability. Traditional approach on the selection of parameters for a adaptive wavelet filter was based on higher order statistics that only present limited statistical information about the bearing fault signals. An adaptive wavelet filter based on the Fractional Lower Order Moment (FLOM) of alpha stable distribution is proposed in this paper. The parameters of the Morlet wavelet filter are optimized based on the principle of maximization of FLOM. The diagnosis results based on the the simulated bearing fault signal with low signal to noise ration (SNR) demonstrated the effectiveness of the proposed approach.