NN-based damage detection in multilayer composites

Authors:
Zhi Wei;Xiaomin Hu;Muhui Fan;Jun Zhang;D. Bi
Affiliations:
School of Mechanical Engineering, Hebei University of Technology, Tianjin, PR China;Department of Computer Science, Sun Yat-sen University, Guangzhou, PR China;School of Mechanical Engineering, Hebei University of Technology, Tianjin, PR China;Department of Computer Science, Sun Yat-sen University, Guangzhou, PR China;Tianjin University of Science and Technology, Tianjin, PR China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Year:
2005

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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.