TY  - CONF
A1  - Ghaznavi, Ali
A1  - Kästle, Emanuel D.
A1  - Popiela, Bartosz
A1  - Duffner, Eric
T1  - Damage monitoring of hydrogen composite pressure vessels using acoustic emission technique and machine learning
T2  - SCHALL 25 – Schallemissionsanalyse und Zustandsüberwachung mit geführten Wellen
N2  - A good understanding of the structural stability of hydrogen composite overwrapped pressure vessels (COPV) is important for the cost-effective design and safe operation of hydrogen storage systems. Acoustic emission (AE) monitoring is a non-destructive method sensitive to microstructural damages such as e.g. fiber breakage, and matrix cracking in COPVs. This study proposes a novel approach for damage monitoring by integrating acoustic emission techniques with machine learning (ML) algorithms to classify and predict damage types in COPVs. However, training accurate classification models requires extensive labeled datasets, which are very challenging to generate due to the nature of AE signal data and the lack of in-situ observations of microscopic failures in COPVs. Our research overcomes this limitation by automating the labeling process of AE signal data for different COPVs using unsupervised ML methods. The most representative features were extracted and then selected from recorded AE signals. Different unsupervised clustering algorithms were utilized based on various extracted feature combinations. The most stable clustering result was achieved and later used as appropriate labels for training classification algorithms. A deep neural network-based deep learning (DL) architecture was used to train discriminative models on AE data, identify patterns, and classify damage types into different classes with improved accuracy and speed for each COPV. Results demonstrate the potential of the proposed combined deep learning approach to train predictive models in identifying failure patterns. The trained models based on individual COPVs show high training, validation, and test accuracy for unseen datasets and offer enhanced predictive capabilities by following advanced DL techniques compared to traditional monitoring methods. The proposed method highlights its potential to improve the efficiency and safety of hydrogen storage systems.
T2  - SCHALL 25
CY  - Dresden, Germany
DA  - 26.03.2025
KW  - Sequential Neural Network
KW  - Acoustic Emission
KW  - Composite Overwrapped Pressure Vessels
KW  - Damagemonitoring
KW  - Machine Learning
KW  - Deep Learning
KW  - Deep Neural Network
PY  - 2025
UR  - https://nbn-resolving.org/urn:nbn:de:kobv:b43-629040
DO  - https://doi.org/10.58286/30958
SP  - 1
EP  - 12
AN  - OPUS4-62904
LA  - eng
AD  - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER  -