关键词: 时域传递路径分析方法, 增广卡尔曼滤波器, 遗传算法, 最小二乘算法

Abstract: When time-domain TPA methods used for tackling vibrations and noise problems in complex systems during transient conditions, which failed to overcome the ill-conditioned problems of the frequency response functions matrix near natural frequencies, and showed low accuracy in extracting the required time-domain information from the existing frequency-domain data. Therefore, a new time-domain TPA method was developed based on AKF. This method started with estimating time-domain operational loads using AKF supplemented by GA, and then identified the impulse response functions using LS algorithm. Finally, time-domain contributions for each transfer path was computed by linearly convolving time-domain operational loads with respective impulse response functions. Case study demonstrates that the load-identification errors of AKF applied in the proposed method are smaller than that of traditional deconvolution filters. Additionally, the errors of impulse response functions identified by LS algorithm are smaller than those obtained by direct inverse fast Fourier transform or creating finite impulse response filters from frequency response functions. Furthermore, the proposed method achieves small errors even in complex structures.

Key words: time-domain transfer path analysis（TPA） method, augmented Kalman filter(AKF), genetic algorithm（GA）, least square（LS） algorithm