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A Machine Learning Based-Guided Wave Approach for Damage Detection and Assessment in Composite Overwrapped Pressure Vessels
- The applications of composite overwrapped pressure vessels (COPVs) in extreme conditions, such as storing hydrogen gases at very high pressure, impose new requirements related to the system's integrity and safety. The development of a structural health monitoring (SHM) system that allows for continuous monitoring of the COPVs provides rich information about the structural integrity of the component. Furthermore, the collected data can be used for different purposes such as increasing the periodic inspection intervals, providing a remaining lifetime prognosis, and also ensuring optimal operating conditions. Ultimately this information can be complementary to the development of the envisioned digital twin of the monitored COPVs. Guided waves (GWs) are preferred to be used in continuous SHM given their ability to travel in complex structures for long distances. However, obtained GW signals are complex and require advanced processing techniques. Machine learning (ML) is increasinglyThe applications of composite overwrapped pressure vessels (COPVs) in extreme conditions, such as storing hydrogen gases at very high pressure, impose new requirements related to the system's integrity and safety. The development of a structural health monitoring (SHM) system that allows for continuous monitoring of the COPVs provides rich information about the structural integrity of the component. Furthermore, the collected data can be used for different purposes such as increasing the periodic inspection intervals, providing a remaining lifetime prognosis, and also ensuring optimal operating conditions. Ultimately this information can be complementary to the development of the envisioned digital twin of the monitored COPVs. Guided waves (GWs) are preferred to be used in continuous SHM given their ability to travel in complex structures for long distances. However, obtained GW signals are complex and require advanced processing techniques. Machine learning (ML) is increasingly utilized as the main part of the processing pipeline to automatically detect anomalies in the system's integrity. Hence, in this study, we are scrutinizing the potential of using ML to provide continuous monitoring of COPVs based on ultrasonic GW data. Data is collected from a network of sensors consisting of fifteen Piezoelectric (PZT) wafers that were surface mounted on the COPV. Two ML algorithms are used in the automated evaluation procedure (i) a long short-term memory (LSTM) autoencoder for anomaly detection (defects/impact), and (ii) a convolutional neural network (CNN) model for feature extraction and classification of the artificial damage sizes and locations. Additional data augmentation steps are introduced such as modification and addition of random noise to original signals to enhance the model's robustness to uncertainties. Overall, it was shown that the ML algorithms used were able to detect and classify the simulated damage with high accuracy.…
Autor*innen: | Amir Charmi, Samir Mustapha, Bengisu YilmazORCiD, Jan Heimann, Jens PragerORCiD |
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Dokumenttyp: | Zeitschriftenartikel |
Veröffentlichungsform: | Graue Literatur |
Sprache: | Englisch |
Titel des übergeordneten Werkes (Englisch): | Research and review journal of nondestructive testing |
Jahr der Erstveröffentlichung: | 2023 |
Organisationseinheit der BAM: | 8 Zerstörungsfreie Prüfung |
8 Zerstörungsfreie Prüfung / 8.4 Akustische und elektromagnetische Verfahren | |
Veröffentlichende Institution: | Bundesanstalt für Materialforschung und -prüfung (BAM) |
Verlag: | NDT.net |
Verlagsort: | Mayen |
Jahrgang/Band: | 1 |
Ausgabe/Heft: | 1 |
Aufsatznummer: | 28079 |
Erste Seite: | 1 |
Letzte Seite: | 6 |
DDC-Klassifikation: | Naturwissenschaften und Mathematik / Chemie / Analytische Chemie |
Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Angewandte Physik | |
Freie Schlagwörter: | COPV; Damage localization; Guided waves; Machine learning; Structural health monitoring |
Themenfelder/Aktivitätsfelder der BAM: | Chemie und Prozesstechnik |
Energie | |
Energie / Wasserstoff | |
Veranstaltung: | 13th European Conference on Non-Destructive Testing (ECNDT) 2023 |
Veranstaltungsort: | Lisbon, Portugal |
Beginndatum der Veranstaltung: | 03.07.2023 |
Enddatum der Veranstaltung: | 07.07.2023 |
DOI: | 10.58286/28079 |
URN: | urn:nbn:de:kobv:b43-590789 |
ISSN: | 2941-4989 |
Verfügbarkeit des Dokuments: | Datei für die Öffentlichkeit verfügbar ("Open Access") |
Lizenz (Deutsch): | Creative Commons - CC BY - Namensnennung 4.0 International |
Datum der Freischaltung: | 11.12.2023 |
Referierte Publikation: | Nein |
Schriftenreihen ohne Nummerierung: | Wissenschaftliche Artikel der BAM |