摘要
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。
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.
作者
张刚
曹莉
贺利芳
易甜
ZHANG Gang;CAO Li;HE Lifang;YI Tian(College of Communications, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;Chongqing Key Lab of Signal and Information Processing, Chongqing 400065, China)
出处
《振动与冲击》
EI
CSCD
北大核心
2019年第9期53-61,共9页
Journal of Vibration and Shock
基金
国家自然科学基金(61671095
61371164
61275099)
重庆市教育委员会科研(KJ1600427
KJ1600429)
关键词
指数型双稳随机共振
经验模态分解
故障信号检测
exponential bi-stable stochastic resonance
empirical mode decomposition (EMD)
fault signal detection
作者简介
第一作者:张刚,男,博士,副教授,1976年;通信作者:曹莉,女,硕士,1992年.
