The ultrasonic guided wave-based method for multi-damage localization has been widely proposed. However, the precision of this method is directly correlated with both the quantity of sensors employed and the intricacy of the implementation process. This relationship poses a challenge in striking a balance between the accuracy and efficiency. To improve the computational efficiency under the premise of ensuring the accuracy of multi-damage localization, this paper proposes a near-field weighted subspace fitting algorithm based on niche-particle swarm optimization. Firstly, the fitting relationship between the signal subspace of the diffraction wave and the array steering vector is established under the uniform linear array. Secondly, a multi-dimensional solution space search algorithm based on niche-particle swarm optimization is proposed to improve the search efficiency of damage. Finally, the algorithm is verified by performance comparison, finite element simulation and experiment. The results show that compared with the same type of method, the algorithm improves the computational efficiency by nearly threefold under the identifiable multi-damage conditions. Additionally, the angle error is 1 ∼ 6°, and the distance error is 1 ∼ 20 mm.

中文翻译：

---

基于小生境粒子群优化的超声导波多损伤定位改进近场加权子空间拟合算法

基于超声导波的多损伤定位方法已被广泛提出。然而，该方法的精度与所使用的传感器的数量和实施过程的复杂性直接相关。这种关系对在准确性和效率之间取得平衡提出了挑战。为了在保证多损伤定位精度的前提下提高计算效率，提出一种基于小生境粒子群优化的近场加权子空间拟合算法。首先建立均匀线阵下衍射波信号子空间与阵列导向矢量的拟合关系。其次，提出一种基于小生境粒子群优化的多维解空间搜索算法，提高损伤搜索效率。最后通过性能比较、有限元仿真和实验对算法进行验证。结果表明，与同类型方法相比，该算法在可识别多重损伤条件下计算效率提高近三倍。另外，角度误差为1～6°，距离误差为1～20mm。