TY - CONF A1 - Mueller, I. A1 - Freitag, S. A1 - Memmolo, V. A1 - Moix-Bonet, M. A1 - Möllenhoff, K. A1 - Golub, M. A1 - Sridaran Venkat, R. A1 - Lugovtsova, Yevgeniya A1 - Eremin, A. A1 - Moll, J. A1 - Tschöke, K. ED - Rizzo, P. ED - Milazzo, A. T1 - Performance Assessment for Artificial Intelligence-Based Data Analysis in Ultrasonic Guided Wave-Based Inspection: A Comparison to Classic Path-Based Probability of Detection T2 - Lecture Notes in Civil Engineering - EWSHM 2022 N2 - Performance assessment for GuidedWave (GW)-based Structural Health Monitoring (SHM) systems is of major importance for industrial deployment. With conventional feature extraction methods like damage indices, pathbased probability of detection (POD) analysis can be realized. To achieve reliability quantification enough data needs to be available, which is rarely the case. Alternatives like methods for performance assessment on system level are still in development and in a discussion phase. In this contribution, POD results using an Artificial Intelligence (AI)-based data analysis are compared with those delivered by conventional data analysis. Using an open-access dataset from Open Guided Wave platform, the possibility of performance assessment for GW-based SHM systems using AI-based data analysis is shown in detail. An artificial neural network (ANN) classifier is trained to detect artificial damage in a stiffened CFRP plate. As input for the ANN, classical damage indicators are used. The ANN is tested to detect damage at another position, whose inspection data were not previously used in training. The findings show very high detection capabilities without sorting any specific path but only having a global view of current damage metrics. The systematic evaluation of the ANN predictions with respect to specific damage sizes allows to compute a probability of correct identification versus flaw dimension, somehow equivalent to and compared with the results achieved through classic path-based POD analysis. Also, sensitive paths are detected by ANN predictions allowing for evaluation of maximal distances between path and damage position. Finally, it is shown that the prediction performance of the ANN can be improved significantly by combining different damage indicators as inputs. T2 - 10th European Workshop on Structural Health Monitoring (EWSHM 2022) CY - Palermo, Italy DA - 04.07.2022 KW - Probability of Detection KW - Composites KW - Open Guided Waves Platform KW - Artificial Neural Network PY - 2022 SN - 978-3-031-07257-4 DO - https://doi.org/10.1007/978-3-031-07258-1 SN - 2366-2557 VL - 2 SP - 953 EP - 961 PB - Springer CY - Cham, Switzerland AN - OPUS4-55269 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -