基于改进确定性采样滤波的疲劳裂纹扩展RUL预测

Research on RUL Prediction of Improved Deterministic Sampling Filter for Fatigue Crack Propagation

针对确定性采样滤波在进行状态估计预测时随维数增加时出现计算量增加且精度不高的问题,提出一种确定性采样滤波的算法并将其应用到疲劳裂纹扩展RUL预测当中去;首先,阐述了确定性采样滤波的基本原理;其次,从多维数值积分的角度分析确定性采样滤波所需计算的数学期望,根据完全对称积分公式计算积分节点值、节点个数和权重;最后,将改进后的确定性采样滤波器应用到构件疲劳裂纹损伤扩展中去,并与无迹卡尔曼滤波算法、容积卡尔曼滤波算法进行比较,提升了裂纹扩展RUL预测的精度,实例仿真分析验证了该方法的可行性和有效性。

In view of the problem of increasing the accuracy of the computation of the deterministic sampling filter in the state estimation, a new algorithm is proposed and applied to the prediction of fatigue crack growth. First of all, the basic principle of determining the sampling filter is described. Secondly, from the point of view of multidimensional numerical integration, the mathematical expectation of deterministic sampling filter is analyzed, and the integral node value, the number of nodes and the weights are calculated according to the fully symmetrical integral formula. Finally, the improved model is applied to the fatigue crack propagation of the members, and the accuracy of the crack prop- agation prediction is improved by comparing with unscented Kalman filtering algorithm and cubature Kalman filtering algorithm. The feasibil- ity and effectiveness of the proposed method are verified by simulation analysis.