Ensemble Supervised Feature Selection for Clustering Damage Modes in CFRP Sandwich Composites Using Acoustic Emission

Acoustic emission (AE) serves as a noninvasive technique for real‐time structural health monitoring, capturing the stress waves produced by the formation and growth of cracks within a material. This study presents a novel ensemble feature selection methodology to rank features highly relevant with damage modes in AE signals gathered from edgewise compression tests on honeycomb‐core carbon fiber‐reinforced polymer. Two distinct features, amplitude and peak frequency, are selected for labeling the AE signals. An ensemble‐supervised feature selection method ranks feature importance according to these labels. Using the ranking list, unsupervised clustering models are then applied to identify damage modes. The comparative results reveal a robust correlation between the damage modes and the features of counts and energy when amplitude is selected. Similarly, when peak frequency is chosen, a significant association is observed between the damage modes and the features of partial powers 1 and 2. These findings demonstrate that, in addition to the commonly used features, other features, such as partial powers, exhibit a correlation with damage modes.