CFRP damage identification system based on FBG sensors and ELM method

Lu, Shizeng; Jiang, Mingshun; Jia, Lei; Sui, Qingmei; Sai, Yaozhang

doi:10.1007/s10043-015-0049-x

Cited by 5 publications

(1 citation statement)

References 16 publications

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“…OS-ELM shows that the learning speed is fast and the generalization performance is good in the large data set. Therefore, combined with previous studies [Chen, Diao and Ren (2014); Lu, Jiang, Jia et al (2015); Zhao, Wu, Zhang et al (2018)], this paper uses the transmissibility function input to the OS-ELM neural network, can be identify damage very effectively and rapidly.…”

Section: Introductionmentioning

confidence: 91%

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

Sun¹,

Yuan²,

Wang³

et al. 2019

Journal of Quantum Computing

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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, increased 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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Section: Introductionmentioning

confidence: 91%