基于改进卡尔曼滤波的结构损伤识别方法研究

Research on structural damage identification method based on improved Kalman filter

传统的卡尔曼滤波算法由于受到历史数据影响较大,导致滤波发散;而遗忘因子滤波算法减少了历史数据的影响。因此,提出了在卡尔曼滤波算法中引入遗忘因子滤波算法识别结构损伤状况。最后采用一个简支梁数值模拟验证该算法的有效性,结果表明,该算法可以有效减少误差、提高识别精度,并具有较好的抗噪性。

The original Kalman filter algorithm resulted in the filter divergence due to the influence of too large old data, while the forgetting factor filtering algorithm reduces the influence of the old data. Therefore, in the pa-per the forgetting factor filtering algorithm is introduced into the Kalman filter algorithm to identify the structural damage. Finally, a simply supported numerical simulation is used to verify the effectiveness of the algorithm. The results show that the algorithm can effectively reduce the error and improve the filtering accuracy, and has satis-factory noise resistance.