Vibration component separation by iteratively using stochastic resonance with different frequency-scale ratios

It is well-known that stochastic resonance (SR) is mainly used for signal denoising and weak signal detection. In this paper, we firstly find the frequency range selection characteristic (filtering characteristic) of re-scaling frequency SR (RFSR) caused by the driving frequency limitation of bistable SR. It then follows that a novel approach to separate vibration components with different frequencies by iteratively using SR is explored. The frequencies of most vibration signals exceed the driving frequency limitation, thus by use of different frequency-scale ratios, the vibration signals with different frequency range can be extracted by RFSR. Firstly, a small frequency-scale ratio is used to obtain the vibration signal with a narrow frequency range, i.e. low frequency vibration. As the output of SR may have a phase lag, a simple phase-shift correction method is proposed to improve the accuracy of signal component separation. The phase-shift corrected signal of RFSR output is separated from the original vibration signal and the residue is treated as the new vibration signal. Then, increasing the frequency-scale ratio according to a searching algorithm, the vibration signal with higher frequency can be obtained by RFSR. Through this iterative process, several harmonic vibration components can be separated from the original noisy vibration signal. The proposed method, empirical mode decomposition (EMD) and Hilbert vibration decomposition (HVD) are respectively applied to analyzing a simulated vibration signal and extracting the fault feature of a rotor system. The contrastive results show that this proposed method has good frequency resolution and can successfully separate monocomponent harmonic signals from a strongly noisy multicomponent harmonic vibration signal while EMD and HVD cannot.