针对传统卡尔曼滤波算法在结构响应重构应用中需要外部激励及测量噪声方差先验已知的问题，提出一种基于增广SVD-MWKF(Singular Value Decomposition-Moving Window Kalman Filter)的激励识别与结构响应重构方法。首先，引入奇异值分解降噪技术以优化移动窗口法对测量噪声方差的实时估计。随后使用基于增广状态空间方程的卡尔曼滤波算法并结合部分测点的加速度测量数据，实现对结构外部激励的识别及各位置的速度、加速度响应的重构。最后，对起重机桁架和简支梁分别进行数值模拟和试验分析，结果表明，相较于移动窗口法，所提方法对测量噪声方差估计更加准确，且对外部激励能进行有效识别。
Excitation Identification and Structural Response Reconstruction Based on Augmented SVD-MWKF
To address the problem that the conventional Kalman filter algorithm requires prior knowledge of external excitation and measurement noise variance in the application of structural response reconstruction,an augmented SVD-MWKF(Singular Value Decomposition-Moving Window Kalman Filter)based excitation identification and structural response reconstruction method is proposed.Firstly,a singular value decomposition noise reduction technique is introduced to optimize the real-time estimation of the measurement noise variance by the moving window method.Secondly,a Kalman filter algorithm based on the augmented state space equation is used to identify the external excitation of the structure and to reconstruct the velocity and acceleration response at each position by combining the acceleration measurement data of some measurement points.Finally,the numerical simulations and experimental analyses are carried out for crane truss and simply supported beam,respectively.The results show that the proposed method is more accurate in estimating the variance of measurement noise than the moving window method,and the external excitations can be effectively identified.
vibration and waveKalman filtering algorithmunknown noise variancesingular value decomposition noise reductionmoving window methodresponse reconstruction
万方数据
维普
