A scalable deep learning approach for robust acoustic emission-based damage classification in composite pressure vessels

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.