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Deep emulational semi-supervised knowledge probability imaging method for plate structural health monitoring using guided waves

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Abstract

Deep networks can obtain the structural state features and optimize the parameters of the feature layer according to the training labels. The training data including the damage signals are quite helpful for detection model training, but sometimes the industrial damage signals are difficult to obtain, especially in airplane skin and other large structures. In this paper, a deep emulational semi-supervised probability imaging algorithm is proposed to present the damage state in the absence of damage samples. A promising signal generation method for simulated damage was implemented through signal encoding, ReLU activation and reconstruction with disturbance, and its effectiveness was verified in metal plate structures and anisotropic composite plate structures. The experiment results illustrate that the proposed method can detect the damage only using normal state signals, presents a good materials generalization in both aluminium plate and composite plate, and has better performance than other state-of-art methods.

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Funding

This work was supported by the National Natural Science Foundation of China under Grant No. 51975220, the National key Research and development program under Grant No. 2019YFB1804200, Guangdong Province Science & Technology project under Grant no. 2018B010109005 and Guangdong Outstanding Youth Fund under Grant No. 2019B151502057, the Fundamental Research Funds for Central Universities project under Grant No. 2019ZD23.

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Authors and Affiliations

  1. School of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou, 510640, China

    Bin Zhang, Dingmin Yang, Xiaobin Hong & Gang Jin

  2. Guangdong Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced Manufacturing, South China University of Technology, Guangzhou, 510641, China

    Gang Jin

Authors
  1. Bin Zhang
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  2. Dingmin Yang
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  3. Xiaobin Hong
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  4. Gang Jin
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Correspondence to Xiaobin Hong.

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Zhang, B., Yang, D., Hong, X. et al. Deep emulational semi-supervised knowledge probability imaging method for plate structural health monitoring using guided waves. Engineering with Computers 38, 4151–4166 (2022). https://doi.org/10.1007/s00366-022-01711-9

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  • DOI: https://doi.org/10.1007/s00366-022-01711-9

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