【摘要】 研究了非平稳信号的时变自回归(TVAR)建模方法,通过引入基函数将非平稳时变参数的辨识转化为线性时不变问题的辨识;在此基础上,应用带遗忘因子的递归最小二乘算法进行参数估计,实现了信号的自适应时频分析。通过仿真算例将该法与短时Fourier变换、W igner分布的结果相比较,验证了该方法时频分辨率高的优越性。最后,将该方法应用于轴承的故障诊断,结果表明,该方法用于故障诊断的特征提取是有效的。更多还原

【Abstract】 Time-varying autoregressive(TVAR) modeling of non-stationary signal is studied.In the proposed method,time-varying parametric identification of non-stationary signal can be translated into a linear time-invariant problem by introduced a set of basis functions.Then,the parameters are estimated using recursive least square algorithm with a forgetting factor and adaptive time-frequency analysis is achieved.The simulation results show that the proposed approach is superior to the short time Fourier transform and Wigner distribution.At last,the proposed method is applied to the fault diagnosis of bearings,the experiment result shows the proposed method is effective in feature extraction.更多还原