Due to the complicacy of geometry and structure in the arched composite structure, it is difficult to monitor various kinds of defects accurately. The developed damage probabilistic diagnostic imaging approach based on ultrasonic guided wave energy signal characteristics is very feasible for the structural health monitoring in the arched composite structures. However, the conventional probabilistic diagnostic imaging (PDI) approaches united with the signal energy damage indices (DIs) have some limitations in the identification of the number, location and specific size information of multi-defects. Thus, the damage shape factor from the single damage-impaired path imminently demands to be majorized to raise the precision and stability of PDI approach in the damage recognition. A corrected probabilistic diagnostic imaging (CPDI) approach integrated with the damage shape factor ${\beta }_M$ needs to be recommended to precisely inspect the expansion of defect zones and different multi-defects in the arched composite structure. The availability and feasibility of the proposed methods has been validated by the experiments in the tested specimen. The results show that the fused frequency-domain energy DIs can be applied to indicate the expansion of defect zones quantitatively. It is proved that the defect identification accuracy of multi-defects from the CPDI approach can be improved by the majorization of damage shape factor, effectively. It is also clearly observed that the number, location and specific size information of different conditions of multi-defects can be distinguished by using the CPDI algorithm, availably.

由于拱形复合结构几何形状和结构的复杂性，难以准确监测各种缺陷。所开发的基于超声导波能量信号特征的损伤概率诊断成像方法对于拱形复合结构的结构健康监测是非常可行的。然而，传统的概率诊断成像 (PDI) 方法结合了信号能量损伤指数 ( DIs))对多缺陷的数量、位置和具体尺寸信息的识别有一定的局限性。因此，迫切需要将来自单一损伤损伤路径的损伤形状因子进行优化，以提高 PDI 方法在损伤识别中的精度和稳定性。一种与损伤形状因子相结合的校正概率诊断成像 (CPDI) 方法${\beta }_{米}$需要推荐精确检测拱形复合结构中缺陷区域的扩展和不同的多缺陷。所提出方法的可用性和可行性已通过测试样本的实验得到验证。结果表明，融合频域能量DIs可用于定量指示缺陷区域的扩展。证明了CPDI方法对多缺陷的缺陷识别精度可以通过损伤形状因子的专业化来有效提高。还清楚地观察到，使用CPDI算法可以有效区分多缺陷不同条件的数量、位置和具体尺寸信息。