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Deep Learning for Time Series-Based Acoustic Emission Damage Classification in Composite Materials

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Abstract

Acoustic emission (AE) technology is of great importance for damage detection and classification in carbon fiber reinforced composite materials. In this study, state-of-the-art deep learning (DL) models for time series were employed to classify three types of damage data, namely fiber breakage, matrix cracking, and delamination, obtained from tensile damage tests. The raw AE time series data were used as inputs to compare the classification performance of eight different deep learning models including FCN, ResNet, XResNet, LSTM_FCN, InceptionTime, XceptionTime, mWDN, and LSTM. The evaluation and analysis showed that XceptionTime, InceptionTime, and ResNet models achieved better training, validation, and testing accuracy, enabling accurate classification of composite material damage. Notably, these three models also demonstrated ideal classification performance for imbalanced data in the composite material AE dataset, providing reference methods for imbalanced data classification problems with small sample sizes in practical engineering applications.

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This work was supported by ongoing institutional funding. No additional grants to carry out or direct this particular research were obtained.

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Authors and Affiliations

  1. College of Mechanical Science and Engineering, Northeast Petroleum University, 163318, Daqing, China

    Fuping Guo, Wei Li, Peng Jiang & Chang Yang

  2. College of Mechanical and Electrical Engineering, Guangdong University of Petrochemical Technology, 525000, Maoming, China

    Fuping Guo & Falin Chen

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  1. Fuping Guo
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Correspondence to Wei Li.

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Guo, F., Li, W., Jiang, P. et al. Deep Learning for Time Series-Based Acoustic Emission Damage Classification in Composite Materials. Russ J Nondestruct Test 59, 665–676 (2023). https://doi.org/10.1134/S1061830923600314

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