The resonant response of a single-degree-of-freedom nonlinear vibro-impact oscillator with a one-sided barrier to a narrow-band random parametric excitation is investigated. The narrow-band random excitation used here...The resonant response of a single-degree-of-freedom nonlinear vibro-impact oscillator with a one-sided barrier to a narrow-band random parametric excitation is investigated. The narrow-band random excitation used here is a bounded random noise. The analysis is based on a special Zhuravlev transformation, which reduces the system to one without impacts, thereby permitting the applications of random averaging over ＂fast＂ variables. The averaged equations are solved exactly and an algebraic equation of the amplitude of the response is obtained for the ease without random disorder. The methods of linearization and moment are used to obtain the formula of the mean-square amplitude approximately for the case with random disorder. The effects of damping, detuning, restitution factor, nonlinear intensity, frequency and magnitude of random excitations are analysed. The theoretical analyses are verified by numerical results. Theoretical analyses and numerical simulations show that the peak response amplitudes will reduce at large damping or large nonlinear intensity and will increase with large amplitude or frequency of the random excitations. The phenomenon of stochastic jump is observed, that is, the steady-state response of the system will jump from a trivial solution to a large non-trivial one when the amplitude of the random excitation exceeds some threshold value, or will jump from a large non-trivial solution to a trivial one when the intensity of the random disorder of the random excitation exceeds some threshold value.展开更多
针对工业环境中随机冲击干扰下滚动轴承微弱故障特征提取难题,提出一种基于自适应短时维纳滤波(Adaptive Short Time Wiener Filtering,ASTWF)和改进正交匹配追踪(Orthogonal Matching Pursuit,OMP)的滚动轴承故障特征提取方法。该方法...针对工业环境中随机冲击干扰下滚动轴承微弱故障特征提取难题,提出一种基于自适应短时维纳滤波(Adaptive Short Time Wiener Filtering,ASTWF)和改进正交匹配追踪(Orthogonal Matching Pursuit,OMP)的滚动轴承故障特征提取方法。该方法首先采用包络峭度和随余比(Random Shocks and Margin Ratio,RMR)作为联合判据,界定窗长界限并自适应确定STWF最优窗长参数,进而将随机冲击干扰从测试信号中分离出来;然后,利用立方包络自相关谱估计信号中周期频率,构造周期原子库,降低匹配原子冗余度;最后,利用相似性理论优化匹配追踪迭代终止条件,并结合周期原子库,实现弱故障冲击特征快速、准确提取。根据仿真信号和通过变速箱下线检测所得工程数据,可验证所提出方法可有效识别随机冲击干扰下的滚动轴承微弱故障特征。对比最小熵形态反卷积(Minimum Entropy Morphological Deconvolution,MEMD)方法对于随机冲击干扰下滚动轴承微弱故障特征提取效果,发现所提出方法具有更好的故障特征提取能力;与经典OMP方法相比,所提出改进OMP方法信号重构速度提升66%。展开更多
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 10772046 and 50978058)Natural Science Foundation of Guangdong Province of China (Grant No. 102528000010000)
文摘The resonant response of a single-degree-of-freedom nonlinear vibro-impact oscillator with a one-sided barrier to a narrow-band random parametric excitation is investigated. The narrow-band random excitation used here is a bounded random noise. The analysis is based on a special Zhuravlev transformation, which reduces the system to one without impacts, thereby permitting the applications of random averaging over ＂fast＂ variables. The averaged equations are solved exactly and an algebraic equation of the amplitude of the response is obtained for the ease without random disorder. The methods of linearization and moment are used to obtain the formula of the mean-square amplitude approximately for the case with random disorder. The effects of damping, detuning, restitution factor, nonlinear intensity, frequency and magnitude of random excitations are analysed. The theoretical analyses are verified by numerical results. Theoretical analyses and numerical simulations show that the peak response amplitudes will reduce at large damping or large nonlinear intensity and will increase with large amplitude or frequency of the random excitations. The phenomenon of stochastic jump is observed, that is, the steady-state response of the system will jump from a trivial solution to a large non-trivial one when the amplitude of the random excitation exceeds some threshold value, or will jump from a large non-trivial solution to a trivial one when the intensity of the random disorder of the random excitation exceeds some threshold value.
文摘针对工业环境中随机冲击干扰下滚动轴承微弱故障特征提取难题,提出一种基于自适应短时维纳滤波(Adaptive Short Time Wiener Filtering,ASTWF)和改进正交匹配追踪(Orthogonal Matching Pursuit,OMP)的滚动轴承故障特征提取方法。该方法首先采用包络峭度和随余比(Random Shocks and Margin Ratio,RMR)作为联合判据,界定窗长界限并自适应确定STWF最优窗长参数,进而将随机冲击干扰从测试信号中分离出来;然后,利用立方包络自相关谱估计信号中周期频率,构造周期原子库,降低匹配原子冗余度;最后,利用相似性理论优化匹配追踪迭代终止条件,并结合周期原子库,实现弱故障冲击特征快速、准确提取。根据仿真信号和通过变速箱下线检测所得工程数据,可验证所提出方法可有效识别随机冲击干扰下的滚动轴承微弱故障特征。对比最小熵形态反卷积(Minimum Entropy Morphological Deconvolution,MEMD)方法对于随机冲击干扰下滚动轴承微弱故障特征提取效果,发现所提出方法具有更好的故障特征提取能力;与经典OMP方法相比,所提出改进OMP方法信号重构速度提升66%。
