Bearing fault diagnosis based on scale-transformation stochastic resonance

Ying Cui; Jun Zhao; Tiantai Guo; Yuqian Song

doi:10.1117/12.2035623

10 October 2013 Bearing fault diagnosis based on scale-transformation stochastic resonance

Ying Cui, Jun Zhao, Tiantai Guo, Yuqian Song

Proceedings Volume 8916, Sixth International Symposium on Precision Mechanical Measurements; 891636 (2013) https://doi.org/10.1117/12.2035623
Event: Sixth International Symposium on Precision Mechanical Measurements, 2013, Guiyang, China

Abstract

A weak fault feature extraction method of rolling bearing based on scale-transformation stochastic resonance (STSR) is proposed. Combined with ensemble empirical mode decomposition (EEMD), the vibration signal with noise is adaptively decomposed for antialiasing by EEMD method to get intrinsic mode functions (IMFs) of different frequency bands, then the IMFs are inputted into scale-transformation mono-stable system. The low frequency fault features are extracted by using a frequency scale R to change the step length of numerical calculation and the adjustment of mono-stable system parameters, and finally slice bi-spectrum is adopted to perform the postprocessing of the output of the mono-stable system. Simulation analysis is performed to validate the characteristics of STSR, and analysis of measured signal of the rolling bearing with strong background noise shows that the approach can extract the weak fault features of rolling bearing successfully.

Citation Download Citation

Ying Cui, Jun Zhao, Tiantai Guo, and Yuqian Song "Bearing fault diagnosis based on scale-transformation stochastic resonance", Proc. SPIE 8916, Sixth International Symposium on Precision Mechanical Measurements, 891636 (10 October 2013); https://doi.org/10.1117/12.2035623

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