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NN-Based Damage Detection in Multilayer Composites

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Advances in Natural Computation (ICNC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3611))

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Abstract

The discrete-time system of multilayer composite plate is modeled using neural network (NN) to produce a nonlinear exogenous autoregressive moving-average model (NARMAX). The model is implemented by training a NN with input-output experimental data. Each damaged sample can be modeled by a parameter governed by the propagation behaviors of the NN. A residual signal is evaluated from the difference between the output of the model and that of the real system. A threshold function is used to detect the damaged behavior of the system. The results show that a three-layer neural network can be a general type of and suitable for the nonlinear input-output mapping problems of multilayer composite system.

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Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300130, PR China

    Zhi Wei & Muhui Fan

  2. Department of Computer Science, Sun Yat-sen University, Guangzhou, 510275, PR China

    Xiaomin Hu & Jun Zhang

  3. Tianjin University of Science and Technology, Box 140, Tianjin, 300222, PR China

    D. Bi

Authors
  1. Zhi Wei
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  2. Xiaomin Hu
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  3. Muhui Fan
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  4. Jun Zhang
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  5. D. Bi
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. School of Software, Sun Yat-Sen University, 510275, Guangzhou, China

    Ke Chen

  3. School of Computer Engineering, Nanyang Technological University, BLK N4, 2b-39, Nanyang Avenue, 639798, Singapore

    Yew Soon Ong

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© 2005 Springer-Verlag Berlin Heidelberg

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Wei, Z., Hu, X., Fan, M., Zhang, J., Bi, D. (2005). NN-Based Damage Detection in Multilayer Composites. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_84

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  • DOI: https://doi.org/10.1007/11539117_84

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28325-6

  • Online ISBN: 978-3-540-31858-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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