Photonic Sensors

, Volume 8, Issue 2, pp 168–175 | Cite as

Research on FBG-Based CFRP Structural Damage Identification Using BP Neural Network

  • Xiangyi Geng
  • Shizeng Lu
  • Mingshun Jiang
  • Qingmei Sui
  • Shanshan Lv
  • Hang Xiao
  • Yuxi Jia
  • Lei Jia
Open Access
Regular
  • 139 Downloads

Abstract

A damage identification system of carbon fiber reinforced plastics (CFRP) structures is investigated using fiber Bragg grating (FBG) sensors and back propagation (BP) neural network. FBG sensors are applied to construct the sensing network to detect the structural dynamic response signals generated by active actuation. The damage identification model is built based on the BP neural network. The dynamic signal characteristics extracted by the Fourier transform are the inputs, and the damage states are the outputs of the model. Besides, damages are simulated by placing lumped masses with different weights instead of inducing real damages, which is confirmed to be feasible by finite element analysis (FEA). At last, the damage identification system is verified on a CFRP plate with 300 mm × 300 mm experimental area, with the accurate identification of varied damage states. The system provides a practical way for CFRP structural damage identification.

Keywords

Carbon fiber reinforced polymer damage identification FBG sensors neural network finite element analysis 

Notes

Acknowledgment

This work was supported by the National Natural Science Foundation of China under Grant Nos. 41472260 and 51373090, the Natural Science Foundation of Shandong Province, China under Grant Nos. 2014ZRE27372 and ZR2017BF007, the Fundamental research funds of Shandong University, China under Grant No. 2016JC012, and the Young Scholars Program of Shandong University 2016WLJH30.

References

  1. [1]
    W. J. Staszewski, S. Mahzan, and R. Traynor, “Health monitoring of aerospace composite structures-active and passive approach,” Composites Science & Technology, 2009, 69(11–12): 1678–1685.CrossRefGoogle Scholar
  2. [2]
    W. Fan and P. Z. Qiao, “Vibration-based damage identification methods: a review and comparative study,” Structural Health Monitoring, 2010, 9(3): 83–111.Google Scholar
  3. [3]
    H. Y. Hwang and C. Kim, “Damage detection in structures using a few frequency response measurements,” Journal of Sound & Vibration, 2004, 270(1–2): 1–14.ADSCrossRefGoogle Scholar
  4. [4]
    E. Kirkby, R. D. Oliveira, V. Michaud, and J. A. Manson, “Impact localisation with FBG for a self-healing carbon fibre composite structure,” Composite Structures, 2011, 94(1): 8–14.CrossRefGoogle Scholar
  5. [5]
    S. R. Di, “Fibre optic sensors for structural health monitoring of aircraft composite structures: recent advances and applications,” Sensors, 2014, 15(8): 18666–18713.Google Scholar
  6. [6]
    P. M. Lam, K. T. Lau, H. Y. Ling, Z. Su, and H. Y. Tam, “Acousto-ultrasonic sensing for delaminated GFRP composites using an embedded FBG sensor,” Optics & Lasers in Engineering, 2009, 47(10): 1049–1055.ADSCrossRefGoogle Scholar
  7. [7]
    T. Okabe and S. Yashiro, “Damage detection in holed composite laminates using an embedded FBG sensor,” Composites Part A: Applied Science & Manufacturing, 2012, 43(3): 388–397.CrossRefGoogle Scholar
  8. [8]
    J. Frieden, J. Cugnoni, J. Botsis, and T. Gmür, “Low energy impact damage monitoring of composites using dynamic strain signals from FBG sensors-part II: damage identification,” Composite Structures, 2012, 94(2): 593–600.CrossRefGoogle Scholar
  9. [9]
    W. Wang, Y. C. Lin, M. R. Zhao, X. Y. Shen, Y. G. Huang, and L. Song, “Damage identification technology based on fiber Bragg grating using SPC and wavelet transform,” Journal of Vibration Measurement & Diagnosis, 2011, 31(5): 566–569.Google Scholar
  10. [10]
    X. J. Chen, Z. F. Gao, and W. Wang, “Application of BP artificial neural network in structure damage identification,” in Proceeding of International Conference on Intelligent Computation Technology and Automation IEEE Computer Society, Changsha, Hunan, China, 2010, pp. 733–737.Google Scholar
  11. [11]
    P. Selva, O. Cherrier, V. Budinger, F. Lachaud, and J. Morlier, “Smart monitoring of aeronautical composites plates based on electromechanical impedance measurements and artificial neural networks,” Engineering Structures, 2013, 56(6): 794–804.CrossRefGoogle Scholar
  12. [12]
    J. C. Li, U. Dackermann, Y. L. Xu, and B. Samali, “Damage identification in civil engineering structures utilizing PCA-compressed residual frequency response functions and neural network ensembles,” Structural Control & Health Monitoring, 2011, 18(2): 207–226.CrossRefGoogle Scholar
  13. [13]
    L. H. Yam, Y. J. Yan, and J. S. Jiang. “Vibration-based damage detection for composite structures using wavelet transform and neural network identification,” Composite Structures, 2003, 60(4): 403–412.CrossRefGoogle Scholar
  14. [14]
    K. O. Hill and G. Meltz, “Fiber Bragg grating technology fundamentals and overview,” Journal of Lightwave Technology, 1997, 15(8): 1263–1276.ADSCrossRefGoogle Scholar
  15. [15]
    T. H. Loutas, A. Panopoulou, D. Roulias, and V. Kostopoulos, “Intelligent health monitoring of aerospace composite structures based on dynamic strain measurements,” Expert Systems with Applications, 2012, 39(9): 8412–8422.CrossRefGoogle Scholar

Copyright information

© The Author(s) 2018

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • Xiangyi Geng
    • 1
  • Shizeng Lu
    • 2
  • Mingshun Jiang
    • 1
  • Qingmei Sui
    • 1
  • Shanshan Lv
    • 1
  • Hang Xiao
    • 1
  • Yuxi Jia
    • 3
  • Lei Jia
    • 1
  1. 1.School of Control Science and EngineeringShandong UniversityJinanChina
  2. 2.School of Electrical EngineeringUniversity of JinanJinanChina
  3. 3.Key Laboratory for Liquid-Solid Structural Evolution & Processing of Materials (Ministry of Education)Shandong UniversityJinanChina

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