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

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

References

[1] Staszewski W. J., Mahzan S., Traynor R.. Health monitoring of aerospace composite structures-active and passive approach. Composites Science & Technology, 2009, 69(11–12): 1678-1685.

[2] Fan W., Qiao P. Z.. Vibration-based damage identification methods: a review and comparative study. Structural Health Monitoring, 2010, 9(3): 83-111.

[3] Hwang H. Y., Kim C.. Damage detection in structures using a few frequency response measurements. Journal of Sound & Vibration, 2004, 270(1–2): 1-14.

[4] Kirkby E., Oliveira R. D., Michaud V., Manson J. A.. Impact localisation with FBG for a self-healing carbon fibre composite structure. Composite Structures, 2011, 94(1): 8-14.

[5] Di S. R.. Fibre optic sensors for structural health monitoring of aircraft composite structures: recent advances and applications. Sensors, 2014, 15(8): 18666-18713.

[6] Lam P. M., Lau K. T., Ling H. Y., Su Z., Tam H. Y.. Acousto-ultrasonic sensing for delaminated GFRP composites using an embedded FBG sensor. Optics & Lasers in Engineering, 2009, 47(10): 1049-1055.

[7] Okabe T., Yashiro S.. Damage detection in holed composite laminates using an embedded FBG sensor. Composites Part A: Applied Science & Manufacturing, 2012, 43(3): 388-397.

[8] Frieden J., Cugnoni J., Botsis J., Gmür T.. 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.

[9] Wang W., Lin Y. C., Zhao M. R., Shen X. Y., Huang Y. G., Song L.. Damage identification technology based on fiber Bragg grating using SPC and wavelet transform. Journal of Vibration Measurement & Diagnosis, 2011, 31(5): 566-569.

[10] Chen X. J., Gao Z. F., Wang W.. Application of BP artificial neural network in structure damage identification. in Proceeding of International Conference on Intelligent Computation Technology and Automation IEEE Computer Society, 2010 733-737.

[11] Selva P., Cherrier O., Budinger V., Lachaud F., Morlier J.. Smart monitoring of aeronautical composites plates based on electromechanical impedance measurements and artificial neural networks. Engineering Structures, 2013, 56(6): 794-804.

[12] Li J. C., Dackermann U., Xu Y. L., Samali B.. 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.

[13] Yam L. H., Yan Y. J., Jiang J. S.. Vibration-based damage detection for composite structures using wavelet transform and neural network identification. Composite Structures, 2003, 60(4): 403-412.

[14] Hill K. O., Meltz G.. Fiber Bragg grating technology fundamentals and overview. Journal of Lightwave Technology, 1997, 15(8): 1263-1276.

[15] Loutas T. H., Panopoulou A., Roulias D., Kostopoulos V.. Intelligent health monitoring of aerospace composite structures based on dynamic strain measurements. Expert Systems with Applications, 2012, 39(9): 8412-8422.