【摘要】 针对粒子滤波应用于结构损伤识别问题时出现的粒子退化、反演计算强不适定性等现象,提出了一种改进的粒子群优化粒子滤波损伤识别方法。在粒子滤波算法中,利用粒子群优化过程驱使粒子群朝着后验概率密度取值较大的区域移动,优化了粒子滤波的采样过程;同时,根据结构损伤参数分布的稀疏性特点,引入对粒子群中损伤参数部分的零变异操作,既增加了粒子的多样性,又有效改善了反问题求解不适定性,提高了算法损伤识别的鲁棒性。数值仿真和框架结构振动实验结果均表明,对于线性或非线性结构,本文方法均能有效抑制噪声干扰,准确识别不同损伤工况下结构损伤的位置与程度;在试验研究中,结构损伤参数识别结果的相对误差小于1.5%。更多还原

【Abstract】 Based on particle swarm optimization(PSO-PF) algorithm, an improved particle filter method is proposed to solve the problems of particles degeneracy, ill-posed characteristics and so on. These problems are common when the particle filter is applied to identify structural damages. The process of particle swarm optimization is used to push particles to move toward the regions with higher posterior probability density, so the important sampling process of particle filter is optimized. Furthermore, according to the sparseness of structural damage parameters distribution, the zero-mutation operation of damage parameters in particles is introduced to maintain the diversity of particles and improve the ill-posedness of inverse problem. Numerical simulations and shaking table test of frame structures show that, for both linear and nonlinear structures, the proposed method in this article can effectively suppress the noise disturbance and accurately identify the position and degree of structural damage under different damage conditions. In the experimental study, the identification relative errors of structural damage parameters are less than 1.5%.更多还原