TY - CONF A1 - Olino, M. A1 - Lugovtsova, Yevgeniya A1 - Memmolo, V. A1 - Prager, Jens T1 - Temperature Compensation Strategies for Lamb Wave Inspection using Distributed Sensor Networks T2 - Proceedings of IEEE International Workshop on Metrology for AeroSpace N2 - The application of temperature compensation strategies is crucial in structural health monitoring approaches based on guided waves. Actually, the varying temperature influences the performance of the inspection system inducing false alarms or missed detection, with a consequent reduction of reliability. This paper quantitatively describes a method to compensate the temperature effect, namely the optimal baseline selection (OBS), extending its application to the case of distributed sensor networks (DSN). The effect of temperature separation between baseline time-traces in OBS are investigated considering multiple couples of sensors employed in the DSN. A combined strategy that uses both OBS and frequent value warning is considered. Theoretical results are compared, using data from two several experiments, which use different frequency analysis with either predominantly A0 mode or S0 mode data or both. The focus is given on the fact that different paths are available in a sensor network and several possible combination of results are available. Nonetheless, introducing a frequent value warning it is possible to increase the efficiency of the OBS approach making use of fewer signal processing algorithms. These confirm that the performance of OBS quantitatively agrees with predictions and also demonstrate that the use of compensation strategies improve detectability of damage. T2 - IEEE International Workshop on Metrology for AeroSpace CY - Pisa, Italy DA - 27.06.2022 KW - Ultrasound KW - Ultrasonic Guided Waves KW - Structural Health Monitoring PY - 2022 SN - 978-1-6654-1076-2 DO - https://doi.org/10.1109/MetroAeroSpace54187.2022.9856029 SP - 598 EP - 601 AN - OPUS4-55268 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - 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 - TY - CONF A1 - Tschöke, K. A1 - Mueller, I. A1 - Memmolo, V. A1 - Sridaran Venkat, R. A1 - Golub, M. A1 - Eremin, A. A1 - Moix-Bonet, M. A1 - Möllenhoff, K. A1 - Lugovtsova, Yevgeniya A1 - Moll, J. A1 - Freitag, S. ED - Rizzo, P. ED - Milazzo, A. T1 - A Model-Assisted Case Study Using Data from Open Guided Waves to Evaluate the Performance of Guided Wave-Based Structural Health Monitoring Systems T2 - Lecture Notes in Civil Engineering - EWSHM 2022 N2 - Reliability assessment of Structural Health Monitoring (SHM) systems poses new challenges pushing the research community to address many questions which are still open. For guided wave-based SHM it is not possible to evaluate the system performance without taking into account the target structure and applied system parameters. This range of variables would result in countless measurements. Factors like environmental conditions, structural dependencies and wave characteristics demand novel solutions for performance analysis of SHM systems compared to those relying on classical non-destructive evaluation. Such novel approaches typically require model-assisted investigations which may not only help to explain and understand performance assessment results but also enable complete studies without costly experiments. Within this contribution, a multi input multi output approach using a sparse transducer array permanently installed on a composite structure to excite and sense guided waves is considered. Firstly, the method and the analysis of path-based performance assessment are presented considering an open-access dataset from the Open Guided Wave platform. Then, a performance analysis of a guided wave-based SHM system using Probability of Detection is presented. To explain some unexpected results, the model-assisted investigations are used to understand the physical phenomena of wave propagation in the test specimen including the interaction with damage. Finally, issues and future steps in SHM systems’ performance assessment and their development are discussed. T2 - 10th European Workshop on Structural Health Monitoring (EWSHM 2022) CY - Palermo, Italy DA - 04.07.2022 KW - Performance assessment KW - Ultrasonic Guided Waves KW - Open Guided Waves Platform 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 - 938 EP - 944 PB - Springer CY - Cham, Switzerland AN - OPUS4-55270 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -