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

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 poi...

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Veröffentlicht in:Journal of quantum computing Jg. 1; H. 1; S. 1 - 8
Hauptverfasser: Sun, Yajie, Yuan, Yanqing, Wang, Qi, Ji, Sai, Wang, Lihua, Wu, Shaoen, Chen, Jie, Zhang, Qin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Henderson Tech Science Press 2019
Schlagworte:
Acceleration
Algorithms
Amplitudes
Artificial neural networks
Composite materials
Damage detection
Impact damage
Machine learning
Neural networks
Noise reduction
Principal components analysis
Signal analysis
Structural damage
ISSN:2579-0145, 2579-0137, 2579-0145
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2579-0145
2579-0137
2579-0145
DOI:10.32604/jqc.2019.05788