Adaptive bistable stochastic resonance for signal denoising
Stochastic resonance (SR) is an important approach to detect weak vibration signal from heavy background noise. In order to increase the calculation speed and improve the weak feature detection performance, a new bistable model is built up. With this model, an adaptive and fast SR method based on dyadic wavelet transform and least square system parameters solving is proposed in this paper. By adding the second-order differential item into the traditional bistable model, the noise utilization can be increased and the quality of SR output signal can be improved. The iteration algorithm for implementing the adaptive SR is given. Compared with the traditional adaptive SR method, this algorithm need not set up the searching range and searching step size of system parameters, and only requires a few iterations. The proposed method, discrete wavelet transform and the traditional adaptive SR method are applied to analyzing simulated vibration signals and extracting the fault feature of a rotor system. The contrastive results verify the superiority of the proposed method, and it can be effectively applied to weak mechanical fault feature extraction.
For the detail, please see: Yi Qin, Yi Tao, Ye He, Baoping Tang. Adaptive bistable stochastic resonance and its application in mechanical fault feature extraction. Journal of Sound and Vibration, 2014, 333(26): 7386-7400.