Weak feature extraction of gear fault based on Stochastic Resonance denoising

摘要：

　　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.

关键词： Cascaded monostable stochastic resonance (CMSR) empirical mode decomposition teager energy operator

作者: LAI Xin-huan ZHAO Jun KONG Ming GUO Tian-tai

作者单位: College of Metrology & Measurement Engineering,China Jiliang University, Hangzhou 310018, China

会议类型: 国际会议

会议名称: 2012第八届精密工程测量和仪器仪表国际研讨会(ISPEMI2012)

会议地点: 成都

会议语种:英文

页码: 1-8

在线出版日期: 2012-08-08（万方平台首次上网日期，不代表论文的发表时间）