在航空領域中，結構在服役過程中可能受到多種因素如疲勞、撞擊和環境侵蝕等影響，進而產生結構損傷。近年來結構健康監測(Structural Health Monitoring, SHM)廣泛應用，本研究提出一種壓電致動之藍姆波，採用具有壓電效應之PZT傳感器黏貼於(Carbon Fiber Reinforced Polymer , CFRP)結構表面，用於激發與接收藍姆波(Lamb wave)，通過對比損傷前後的訊號差異，可以有效地識別和定位結構內的損傷。
本研究基於能量法計算損傷指數(Damage Index, DI)，並將各路徑定義成相對應的DI值。利用卡方分布進行計算各路徑之p值(p value)並保留低於特定閾值之路徑，通過保留路徑所產生之交點使用核密度估計(Kernel Density Estimation, KDE)分別對於x與y軸進行繪製核密度曲線，從而區分出單一與多重損傷情況，同時排除因路徑重疊所產生的誤判交點，最終根據篩選後的交點進行確定潛在損傷的位置。
本文提出損傷符號N_s與加權分數β以進行損傷定位指標(damage localization index, DLI)計算，並與文獻演算法進行比較，結果顯示本研究演算法於準確率高於文獻演算法約15%，並比較各演算法之多重損傷判定方法、閥值挑選、損傷範圍繪製，提供一種對於多重損傷判定更靈活的用法。

In aerospace applications, structural are vulnerable to factors like fatigue, impact, and environmental corrosion, cause structural damage. Structural health monitoring (SHM) has been widely applicable in recent years. This study uses PZTs with piezoelectric effect used to be adhered on the surface of Carbon Fiber Reinforced Polymer (CFRP) to generate and receive the Lamb waves. By analyzing wave dispersions with the existence of the damage, its location can be effectively identified.
The Damage Index (DI) is calculated based on energy method. With each path assigned a DI value, it forms a chi-squared distribution. For a path that its p-value is less than a threshold, it is preserved for damage localizations. By using Kernel Density Estimation (KDE), the probability density curves along the x and y axes from the intersections of preserved paths. Based on algorithm, one can distinguish the situation of one-damage or multiple-damage in one subset. The false intersection by overlap paths can be also eliminated. Ultimately, the potential damage localizations are identified from the filtered intersection points.
This research proposed damage symbols (N_s) and weighted score (β) to compute the damage localization index (DLI), which is compared with the existing algorithm in the literature. The results show a 15% increase in accuracy by the comparisons. The damage localization method, threshold selection and damage range determination are also compared among various algorithms, providing a more flexible approach to identify multiple-damage localizations.

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