指数型随机共振微弱振动信号检测方法

在实际工程故障诊断中特征频率信号经常淹没在噪声中,信息提取非常困难。为了提取强噪声背景中的微弱信号,将简谐势阱与Gaussian Potential模型相结合,提出一种作用在Duffing方程下的新型指数型双稳随机共振系统。首先,推导逃逸率并研究系统参数对输出信噪比影响;其次,基于指数型双稳随机共振系统对冲击衰减信号以及谐波振动信号进行检测;最后为检测大噪声下多频信号提出指数型双稳随机共振和经验模态分解的微弱信号联合检测方法并应用于轴承故障信号检测中。实验分析及仿真结果表明,指数型双稳随机共振模型在信号检测中是可行的,并且对于多频谐波信号通过随机共振后进行经验模态分解可使检测更加准确,联合检测不仅能识别故障信号,还能识别故障倍频信号。

Weak vibration signal detection method for exponential stochastic resonance systems

In actual Engineering fault diagnosis, feature frequency signals of faults are often submerged in noise, their information extraction is very difficult. Here, in order to extract weak signals in background of strong noise, combining the simple harmonic potential trap and Gaussian potential model, a new exponential bi-stable stochastic resonance system acting on Duffing equation was proposed. Firstly, the escape rate was derived and the effects of the system’s parameters on the output signal-to-noise ratio were studied. Then, harmonic vibration signals and impact attenuation signals were detected based on the exponential bi-stable stochastic resonance system. Finally, the weak signal combination detection method of the exponential bi-stable stochastic resonance and the empirical mode decomposition (EMD) was proposed to detect weak multi-frequency signals under heavy noise, and this method was applied in bearing fault signal detection. Test analysis and simulation results showed that the exponential bi-stable stochastic resonance model is feasible in signal detection;EMD is performed on multi-frequency harmonic signals after passing through stochastic resonances to make the detection be more accurate;the combination detection method can be used not only to recognize fault signals, but also recognize fault double-frequency ones.