Online monitoring of crack depth in fiber reinforced composite beams using optimization Grey model GM(1,N)

The existing analytical and numerical simulation models are not able to estimate the crack depth when poor information available about the crack. The present study is aimed to develop an online monitoring system to estimate crack depth in composites. The online monitoring system is developed with optimization Grey model OGM(1,N) and support vector machine (SVM) sepa-rately and the crack depth is estimated in E-glass fiber reinforcement polymer composites. In this study, cracks are made artificially on the E-glass fiber reinforcement polymer at distance of 50, 100 and 150 mm from free end with crack depth ratios of 12.9%, 14.1%, 15.3%, 16.5%, 17.6% and 18.8%. Natural frequency is measured at three nodes for all the cracks. The proposed SVM and GN(1,N) models are trained with four samples for each position and tested for the remaining two samples. In the proposed OGM(1,N) model, training samples are updated by adding recent data sample and deleting old data. So that the OGM(1,N) model predicts the crack depth ratio more accurately than the SVM model with an error of 1.06%. The OGM (1, N) is simple and directly estimates the crack depth by taking into account online measured data of the vibration frequency. Based on the accuracy in prediction, the Grey online modeling and monitoring system is suggested to estimated crack depth in composites. Interaction effect of the crack position and crack depth ratio on the natural frequency at the three nodes is studied.