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Performance Assessment for Artificial Intelligence-Based Data Analysis in Ultrasonic Guided Wave-Based Inspection: A Comparison to Classic Path-Based Probability of Detection

  • 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 arePerformance 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.zeige mehrzeige weniger

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Autor*innen:I. Mueller, S. Freitag, V. Memmolo, M. Moix-Bonet, K. Möllenhoff, M. Golub, R. Sridaran Venkat, Yevgeniya Lugovtsova, A. Eremin, J. Moll, K. Tschöke
Persönliche Herausgeber*innen:P. Rizzo, A. Milazzo
Dokumenttyp:Beitrag zu einem Tagungsband
Veröffentlichungsform:Graue Literatur
Sprache:Englisch
Titel des übergeordneten Werkes (Englisch):Lecture Notes in Civil Engineering - EWSHM 2022
Jahr der Erstveröffentlichung:2022
Organisationseinheit der BAM:8 Zerstörungsfreie Prüfung
8 Zerstörungsfreie Prüfung / 8.4 Akustische und elektromagnetische Verfahren
Verlag:Springer
Verlagsort:Cham, Switzerland
Jahrgang/Band:2
Erste Seite:953
Letzte Seite:961
DDC-Klassifikation:Naturwissenschaften und Mathematik / Chemie / Analytische Chemie
Freie Schlagwörter:Artificial Neural Network; Composites; Open Guided Waves Platform; Probability of Detection
Themenfelder/Aktivitätsfelder der BAM:Chemie und Prozesstechnik
Veranstaltung:10th European Workshop on Structural Health Monitoring (EWSHM 2022)
Veranstaltungsort:Palermo, Italy
Beginndatum der Veranstaltung:04.07.2022
Enddatum der Veranstaltung:07.07.2022
DOI:10.1007/978-3-031-07258-1
ISSN:2366-2557
ISBN:978-3-031-07257-4
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:18.07.2022
Referierte Publikation:Ja
Datum der Eintragung als referierte Publikation:14.11.2022
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