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中文摘要：
一个非静止的信号当模特儿的变化时间的 autoregressive (TVAR ) 被学习。在建议方法，一个非静止的信号的变化时间的参量的鉴定能被介绍一套基本功能翻译成一个线性时间不变的问题。然后，参数被与一个忘记的因素使用一个递归的最不方形的算法估计，适应时间频率分布被完成。模拟结果证明建议途径比短时间的 Fourier 变换和 Wigner 分发优异。并且最后，建议方法被用于适用的差错诊断，并且实验结果证明建议方法在特征抽取是有效的。
英文摘要：
The time-varying autoregressive （TVAR） modeling of a non-stationary signal is studied. In the proposed method, time-varying parametric identification of a non-stationary signal can be translated into a linear time-invariant problem by introducing a set of basic functions. Then, the parameters are estimated by using a recursive least square algorithm with a forgetting factor and an adaptive time-frequency distribution is achieved. The simulation results show that the proposed approach is superior to the short-time Fourier transform and Wigner distribution. And finally, the proposed method is applied to the fault diagnosis of a bearing , and the experiment result shows that the proposed method is effective in feature extraction.
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