Weak feature extraction of gear fault based on stochastic resonance denoising

Jun Zhao; Xin-huan Lai; Ming Kong; Tian-tai Guo

doi:10.1117/12.2015034

31 January 2013 Weak feature extraction of gear fault based on stochastic resonance denoising

Jun Zhao, Xin-huan Lai, Ming Kong, Tian-tai Guo

Proceedings Volume 8759, Eighth International Symposium on Precision Engineering Measurement and Instrumentation; 87592X (2013) https://doi.org/10.1117/12.2015034
Event: International Symposium on Precision Engineering Measurement and Instrumentation 2012, 2012, Chengdu, China

Abstract

To solve the problem of feature extraction of weak gear fault under strong noise background, an early feature extraction method based on cascaded monostable stochastic resonance (CMSR) system and empirical mode decomposition (EMD) with teager energy operator demodulation was proposed. The model of monostable stochastic resonance expanded the processing range of characteristic frequency of the measured signal, and had a good effect on denoising performance by cascading. Firstly CMSR was employed as the preprocessor to remove noise, then the denoised signal was decomposed into a series of intrinsic mode functions (IMFs) of different scales by EMD, and finally teager energy operator demodulation was applied to obtain amplitudes and frequencies of each effective IMF to extract the weak gear fault feature. Simulation and application results showed that the proposed method could effectively detect the characteristic frequency of gear fault of local damage after the noise reduction by CMSR.

Citation Download Citation

Jun Zhao, Xin-huan Lai, Ming Kong, and Tian-tai Guo "Weak feature extraction of gear fault based on stochastic resonance denoising", Proc. SPIE 8759, Eighth International Symposium on Precision Engineering Measurement and Instrumentation, 87592X (31 January 2013); https://doi.org/10.1117/12.2015034

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