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.
Highlights
Carbon fiber reinforced polymer (CFRP) has shown great potential in the fields of aeronautics, automotive and civil engineering for its outstanding performances [1]
The wavelength of the fiber Bragg grating (FBG) pasted on the structural surface will shift because of the structural dynamic strain actuated by active excitation approach, which means that FBG sensors can accurately detect the dynamic response signals containing the structural damage information
The damage identification method of CFRP structures based on the FBG sensors and back propagation (BP) neural network is investigated and experimentally verified
Summary
Carbon fiber reinforced polymer (CFRP) has shown great potential in the fields of aeronautics, automotive and civil engineering for its outstanding performances [1]. An inverse numerical-experimental method was developed in [8] to identify the damage based on natural frequency changes measured by FBG sensors. Li et al [12] presented a thorough investigation into a vibration-based damage identification method utilizing dimensionally reduced residual frequency response function data in combination with neural networks to identify locations and severities of damage in numerical and experimental beam structures. Yam et al [13] utilized the neural network to establish the mapping relationship between the energy variation of the structural vibration responses and damage status (location and severity), realizing damage detection for polyvinyl chloride (PVC) sandwich plates. The composite laminate of different damage states is excited by active actuation, and the FBG sensor network is applied to detect the dynamic responses. Damages in this paper are simulated by placing lumped masses with different weights, instead of inducing real damages, providing an easy and cost saving way for experimental researches
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