基于级联双稳随机共振和多重分形的机械故障诊断方法研究  

Mechanical fault diagnosis based on cascaded bistable stochastic resonance and multi-fractal

作　　者：郝研[1] 王太勇[1,2] 万剑[2] 张攀[2] 

机构地区：[1]天津大学精密仪器与光电子工程学院,天津300072 [2]天津大学机械工程学院,天津300072

出　　处：《振动与冲击》2012年第8期181-185,共5页Journal of Vibration and Shock

基　　金：国家自然科学基金(50975193);国家科技重大专项(2009ZX04014-101-05);教育部2010年博士点基金(20100032110006)

摘　　要：对级联双稳随机共振的滤波特性进行了对比和分析,利用这种特性,结合广义维数对信号非线性特征的度量能力,提出了基于级联双稳随机共振和多重分形的机械故障诊断方法。实验结果证明,该方法可以有效地消除高频噪声,增强低频段信号的能量,由此得到的分形维数比较准确,能够更加精确地度量机械振动信号的非线性特征,从而达到机械故障诊断的目的。The filtering performance of cascaded bistable stochastic resonance （CBSR） was analyzed. Depending on the filtering feature of CBSR and the measurement capability of generalized dimension for non-linear characteristics of signals, a method of mechanical fault diagnosis based on CBSR and multi-fractal was presented. The experiment results showed that this method can not only remove high frequency noise efficiently but also enhance the energy of low frequency signals, the obtained fractal dimension is more correct; the fractal dimension can measure non-linear characteristics of mechanical vibration signals accurately in order to implement mechanical fault diagnosis.

关 键 词：信息处理技术 级联双稳随机共振 多重分形 广义维数 故障诊断 滤波 

分 类 号：TN911.72[电子电信—通信与信息系统]