基于贝叶斯估计的动载荷识别方法

摘要

为了降低测量误差等不确定性因素对识别结果的影响,建立基于贝叶斯估计理论的动力学系统载荷识别方法.首先,根据动力学系统运动方程,利用贝叶斯理论,推导载荷和误差参数的联合后验分布,进而得到载荷和误差参数的边缘概率分布;然后,采用马尔可夫蒙特卡罗方法,估计动力学系统所受的载荷,并利用仿真算例与基于奇异值分解的载荷识别方法进行对比;最后,利用实验数据,进一步验证本方法的有效性.结果表明,该方法在一定程度上减小了不确定性因素造成的识别误差,对于提高动载荷识别精度具有一定的参考意义.%In order to reduce the effects of the measurement errors and some other uncertainty factors on the accuracy of identification results, a dynamic load identification approach based on Bayesian estimation theory is presented. Firstly, based on the kinematic equations of the dynamic system,the posterior joint probability density functions of the load and the error parameter are formulated using Bayesian estimation, and their posterior marginal probability density functions are obtained. Then, the Markov Chain Monte Carlo methods are employed to estimate the load of the dynamic system. The proposed approach is evaluated through comparing the obtained load results with those by the methods based on SVD. Finally,experimental measurements are performed to examine the efficiency of the approach.The results demonstrate that this approach can reduce the effects of the errors caused by uncertainties on the accuracy of identification results. This approach may provide a reference for improving the accuracy of identification results.