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Hybrid Intelligent Damage Identification of Composite Plate Based on Ensemble Empirical Mode Decomposition and Support Vector Machine

Hybrid Intelligent Damage Identification of Composite Plate Based on Ensemble Empirical Mode Decomposition and Support Vector Machine
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摘要 In this paper,a novel method based on strain distribution is presented to determine the presence of damage and its location in composite plate.By building a damage monitoring experimental platform with Fiber Bragg Gratings(FBGs)sensors,impact experiments are made respectively to gain the strain distribution both in heath and damage state.EEMD is used to process the data and IMFs energy feature is evaluated.Then,support vector machine is applied to identify the damage and the testing classification accuracy reaches 92.86%.Finally,by using the influence of the damage position and the propagation path on energy,the damage location is predicted.The experimental results indicate that the proposed method can accurately identify the presence and position of damage.The effectiveness and reliability of the proposed method is verified. In this paper ,a novel method based on strain distribution is presented to determine the presence of damage and its location in composite plate .By building a damage monitoring experimental platform with Fiber Bragg Gratings (FBGs) sensors,impact experiments are made respectively to gain the strain distribution both in heath and damage state . EEMD is used to process the data and IMFs energy feature is evaluated .Then,support vector machine is applied to identify the damage and the testing classification accuracy reaches 92.86%.Finally,by using the influence of the damage position and the propagation path on energy ,the damage location is predicted .The experimental results indicate that the proposed method can accurately identify the presence and position of damage .The effectiveness and reliability of the proposed method is verified.
出处 《纤维复合材料》 CAS 2013年第4期3-7,共5页 Fiber Composites
基金 supported by the National Natural Science Foundation of China(No.51175401) the Program for Changjiang Scholars and Innovative Research Team in University
关键词 composite materials FBG EEMD support vector machine composite materials FBG EEMD support vector machine
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  • 1N. Cristianini and J. Shawe-Taylor.Support vector machines and other kernel-based learning methods[]..2000
  • 2Z. R. Yuan.Artificial neural network and its application[]..2000

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