Realizing the empirical mode decomposition by the adaptive stochastic resonance in a new periodical model and its application in bearing fault diagnosis

We investigate a multi-frequency signal that cannot be decomposed by empirical mode decomposition directly. Moreover, this kind of signal in the noisy background cannot be decomposed successfully by the traditional stochastic resonance with bistable system yet. We propose a new method which using the empirical mode decomposition combined the adaptive stochastic resonance in a new periodical model to solve this problem. The results show that the proposed method decomposes the multi-frequency signal perfectly. Meanwhile, the general scale transformation and random particle swarm optimization algorithm are used to help obtain a better result in the process of optimization. Through using this new method, the simulation results are satisfactory. More importantly, this new method also shows good performance in the application of bearing fault diagnosis.