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Damage detection combining principal component analysis and deep convolutional neural network with dynamic response from FBG arrays

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Abstract

With the breakthrough of deep learning in vibration-based damage detection, such methods have obtained high detection accuracy, especially supervised learning. However, the work of labelling data is tedious and challenging when facing complex structures and long-time monitoring. This paper proposes a damage detection method based on principal component analysis (PCA) and deep convolutional neural network (DCNN) with dynamic response measured by FBG (Fibre Bragg Grating) sensor arrays. Several FBG sensors are applied to form chainlike arrays along a steel beam and a reinforced concrete (RC) beam. The raw dynamic signal is recorded via FBG sensors in different damage states and analyzed by PCA-based method focusing on T2 and Q statistic as damage indices firstly. Then a priori knowledge of damage in each raw data is achieved according to calculated damage indices and the raw data are labelled. After the labelling procedure, DCNN-based models for steel beam and RC beam are constructed and trained. The DCNN models are evaluated and tested to predict the unknown damage levels. The results show that the DCNN-based damage detection method with the training data labelled by PCA-based model can accurately predict the damage levels.

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Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 51308369), A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) and an Enterprise graduate workstation ‘SUST-CCCC First Highway Two Engineering CO., LTD.’.

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Authors and Affiliations

  1. School of Civil Engineering, Suzhou University of Science and Technology, Suzhou, 215011, China

    Dapeng Wang & Wenda Zhang

  2. Key Laboratory of Structural Engineering of Jiangsu Province, Suzhou, 215011, China

    Dapeng Wang

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  1. Dapeng Wang
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  2. Wenda Zhang
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Correspondence to Dapeng Wang.

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Wang, D., Zhang, W. Damage detection combining principal component analysis and deep convolutional neural network with dynamic response from FBG arrays. J Civil Struct Health Monit 13, 101–115 (2023). https://doi.org/10.1007/s13349-022-00621-0

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  • DOI: https://doi.org/10.1007/s13349-022-00621-0

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