Open Access

ARTICLE

Impact Damage Identification for Composite Material Based on Transmissibility Function and OS-ELM Algorithm

Yajie Sun^1,2,*, Yanqing Yuan², Qi Wang², Sai Ji^1,2, Lihua Wang³, Shaoen Wu⁴, Jie Chen², Qin Zhang²

1 Jiangsu engineering Centre of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
2 School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
3 School of Information and Control, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
4 Department of Computer Science, Ball State University, Muncie, USA.

*Corresponding Author: Yajie Sun. Email: .

Journal of Quantum Computing 2019, 1(1), 1-8. https://doi.org/10.32604/jqc.2019.05788

Abstract

A method is proposed based on the transmissibility function and the Online Sequence Extreme Learning Machine (OS-ELM) algorithm, which is applied to the impact damage of composite materials. First of all, the transmissibility functions of the undamaged signals and the damage signals at different points are calculated. Secondly, the difference between them is taken as the damage index. Finally, principal component analysis (PCA) is used to reduce the noise feature. And then, input to the online sequence limit learning neural network classification to identify damage and confirm the damage location. Taking the amplitude of the transmissibility function instead of the acceleration response as the signal analysis for structural damage identification cannot be influenced by the excitation amplitude. The OS-ELM algorithm is based on the ELM (Extreme Learning Machine) algorithm, in-creased training speed also increases the recognition accuracy. Experiment in the epoxy board shows that the method can effectively identify the structural damage accurately.

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Keywords

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