【摘要】 针对航天器非平稳随机振动信号模态频率密集的特点,提出了基于经验模式分解EMD(Empirical Mode Decomposition)的多分量过程神经网络PNN(Process Neural Net-work)自回归模型.通过EMD对原始时间序列进行分解,使之成为一组不同尺度的局部正交本征模函数IMF(Intrinsic Mode Functions),利用PNN对每个IMF分别进行时变参数分析并以此确定其时变自功率谱密度,对所有分量的时变自功率谱密度通过叠加进行重构,以此得到原始信号的时变自功率谱密度.仿真结果和实例分析表明:和传统的时频分析法相比,该方法直接使用信号数据,避免了相关估计计算,减小了计算工作量;无交叉干扰项,提高了信号的时频分布特性,具有较高的时频分辨率;对各工况下航天器的振动信号能有效的进行分析,具有较强的信号特征提取能力.更多还原

【Abstract】 In view of the disadvantages of the traditional time-varying parameters modeling algorithm about nonstationary random vibration signal of a spacecraft with closed spaced modal frequency,a multicomponent process neural network(PNN) autoregressive model was proposed,which was based on the empirical mode decomposition(EMD).The EMD was utilized to decompose the original time series into several local orthogonal intrinsic mode functions(IMF) with different time scale.The PNN was established for anyone of these IMF,and obtained time-varying power spectrum density(PSD).The time-varying PSD of the original signal was reconstituted by superposing.The simulation and example analyzed results suggest that this method avoids the correlative estimation calculation and reduces the calculation complexity.There is no cross-term interference,so it improves the time-frequency distributing characteristic of the signals.This method has a higher ability of extracting signal characteristic and can be used to analyze the vibration signals of spacecraft under various work conditions.更多还原