TY - CONF A1 - Ghaznavi, Ali T1 - Damage monitoring of hydrogen composite pressure vessels using acoustic emission technique and machine learning 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 - Acoustic Emission KW - Composite Overwrapped Pressure Vessels, KW - Damage monitoring KW - Machine Learning KW - Deep Learning KW - Deep Neural Network KW - Sequential Neural Network PY - 2025 AN - OPUS4-62915 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Ghaznavi, Ali T1 - Machine Learning Approach for Robust Acoustic Emission-Based Damage Classification in Pressure Vessels N2 - Accurate damage classification of Composite Pressure Vessels (CPVs) is crucial for understanding failure behaviour of hydrogen storage systems. Acoustic Emission (AE) monitoring is a non-destructive testing technique capable of detecting signals from different failure mechanisms such as fiber breakage and matrix cracking, supporting durability assessment of CPVs. Therefore, the main objective of this study is to combine AE and advanced deep learning techniques to develop a robust framework for automatic and accurate identification and classification of damage mechanisms across various CPVs. The evolutionary Genetic Algorithms (GA) was used for feature selection, followed by unsupervised clustering to generate automatic labels for model training. Two different FCNN and CNN-LSTM architectures were used to train individual models based on different AE datasets. Later, Adaptive Transfer Learning (ATL) and Meta Ensemble Learning (MEL) techniques were applied to handle data variability and train predictive generalized model over varied AE datasets. The ATL fine-tunes a pre-trained models to leverage their knowledge, while MEL uses pre-trained models' predictions as meta features to train a meta model. Experimental results demonstrate that while both generalized ATL and MEL trained models perform well across different AE datasets, the MEL framework outperforms ATL method in terms of evaluation metrics. The Mean-Accuracy score reaches 0.9026, and 0.9900 for ATL, and MEL, respectively. The most accurate multi-class classification results was achieved using MEL method in terms of the Mean-Accuracy and Recall metrics. The proposed framework provides a scalable, adaptive approach for automated damage classification using AE signals across diverse CPVs in real-world settings. T2 - BAM Colloquium Abteilung 3 CY - Berlin, Germany DA - 14.10.2025 KW - Acoustic Emission KW - Machine Learning KW - Sequential Neural Network KW - Deep Learning KW - Deep Neural Network PY - 2025 AN - OPUS4-65184 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Kästle, Emanuel A1 - Duffner, Eric A1 - Ghaznavi, Ali T1 - DAVID Project meeting 23.09.2025 N2 - Yearly report on the DAVID project, presented in front of the consortial partners. The report shows the progress achieved by the DAVID project team at BAM in terms of testing newly developed carbon-fiber reinforced pressure vessels of type IV. T2 - DAVID project meeting CY - Berlin, Germany DA - 23.09.2025 KW - Acoustic Emission KW - Pressure Vessels KW - DAVID project KW - Damage monitoring KW - Fiber reinforced polymers PY - 2025 AN - OPUS4-65289 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -