Research on stochastic Resonation-Hilbert Transform method for fault diagnosis of rolling Bearings

Abstract

Aiming at the problem that the initial weak fault characteristic signals of turbines rolling bearing are difficult to be extracted under strong background noise, this paper has proposed a fault feature extraction method based on stochastic resonance and Hilbert Transform. Firstly, modulation and demodulation technology are combined with the classical stochastic resonance detection method to design an optimal matched stochastic resonance detection system suitable for multi-frequency weak periodic signals. The system is used to reduce and enhance the noise of rolling bearing signals. Then the fault signal envelope is extracted by Hilbert transform and the fault characteristic frequency is extracted by spectral analysis. Simulation analysis and experimental data show that this method has good noise reduction ability and can effectively extract the weak fault signal feature. The extracted characteristic frequency is consistent with the theoretical analysis.