Novelty detection and dimension reduction via guided ultrasonic waves: Damage monitoring of scarf repairs in composite laminates

This work focuses on structural health monitoring aspects of composite adhesively bonded repairs, evaluating their performance with guided ultrasonic waves. These repairs have shown remarkable potential in addressing repairability demands in new composite aircraft. More specifically, the behavior of a scarf repair under axial tensile loading was monitored with guided ultrasonic waves. The signal post-processing techniques focused on the extraction of the appropriate features, on the application of the pattern recognition and dimension reduction algorithms and on their subsequent correlation with the damage. A principal component analysis was employed that operated as a benchmark for the proposal of a more advanced data reduction method, the nonlinear principal component analysis. Appropriate damage indices were extracted and the results were correlated with images taken through a digital image correlation technique. The correlation of the extracted features with the early stage damage was performed and conclusions about the recovered strength through the scarf repair were deduced. The study focused on the selection of appropriate signal features and on their subsequent investigation through an outlier analysis. The limits of the applied outlier analysis were interpreted through principal component analysis and optimized through the concept of principal curves as derived through the nonlinear principal component analysis.