Conducting damage diagnostics on stiffened panels is commonly performed using a single SHM technique. However, each SHM technique has both its strengths and limitations. Rather than straining the expansion of single SHM techniques going beyond their intrinsic capacities, these strengths and limitations should instead be considered in their application. In this work, we propose a novel fusion-based methodology between data from two SHM techniques in order to surpass the capabilities of a single SHM technique. The aim is to show that by considering data fusion, a synergy can be obtained, resulting in a comprehensive damage assessment, not possible using a single SHM technique. For this purpose, three single-stiffener carbon–epoxy panels were subjected to fatigue compression after impact tests. Two SHM techniques monitored damage growth under the applied fatigue loads: acoustic emission and distributed fiber optic strain sensing. Four acoustic emission sensors were placed on each panel, thereby allowing for damage detection, localization, type identification (delamination), and severity assessment. The optical fibers were adhered to the stiffener feet’ surface, and its strain measurements were used for damage detection, disbond localization, damage type identification (stiffness degradation and disbond growth), and severity assessment. Different fusion techniques are presented in order to integrate the acoustic emission and strain data. For damage detection and severity assessment, a hybrid health indicator is obtained by feature-level fusion while a complementary and cooperative fusion of the diagnostic results is developed for damage localization and type identification. We show that damage growth can be monitored up until final failure, thereby performing a simultaneous damage assessment on all four SHM levels. In this manner, we demonstrate that by proposing a fusion-based approach toward SHM of composite structures, the intrinsic capacity of each SHM technique can be utilized, leading to synergistic effects for damage diagnostics.

通常使用单个SHM技术在加硬板上进行损坏诊断。但是，每种SHM技术都有其优势和局限性。这些强度和局限性应在其应用中加以考虑，而不是使单个SHM技术的扩展超出其固有能力。在这项工作中，我们提出了一种来自两种SHM技术的数据之间基于融合的新颖方法，以超越单个SHM技术的功能。目的是表明通过考虑数据融合，可以获得协同作用，从而导致全面的损害评估，而使用单个SHM技术则无法实现。为此，在冲击试验后对三块单筋碳环氧板进行了疲劳压缩。两种SHM技术可在施加的疲劳载荷下监控损伤的增长：声发射和分布式光纤应变传感。每个面板上放置了四个声发射传感器，从而可以进行损坏检测，定位，类型识别（分层）和严重性评估。将光纤粘附到加劲肋脚的表面，并将其应变测量值用于损伤检测，脱粘局部化，损伤类型识别（刚度降低和脱粘生长）以及严重性评估。为了整合声发射和应变数据，提出了不同的融合技术。为了进行损坏检测和严重性评估，通过特征级融合获得混合健康指示器，同时开发诊断结果的互补和协作融合以进行损伤定位和类型识别。我们表明，直到最终故障之前，都可以监视破坏的增长，从而对所有四个SHM级别执行同时的破坏评估。通过这种方式，我们证明了通过对复合结构的SHM提出基于融合的方法，可以利用每种SHM技术的固有能力，从而为损伤诊断带来协同效应。