Crack identification in functionally graded material framed structures using stationary wavelet transform and neural network

Abstract

In this paper, an integrated procedure is proposed to identify cracks in a portal framed structure made of functionally graded material (FGM) using stationary wavelet transform (SWT) and neural network (NN). Material properties of the structure vary along the thickness of beam elements by the power law of volumn distribution. Cracks are assumed to be open and are modeled by double massless springs with stiffness calculated from their depth. The dynamic stiffness method (DSM) is developed to calculate the mode shapes of a cracked frame structure based on shape functions obtained as a general solution of vibration in multiple cracked FGM Timoshenko beams. The SWT of mode shapes is examined for localization of potential cracks in the frame structure and utilized as the input data of NN for crack depth identification. The integrated procedure proposed is shown to be very effective for accurately assessing crack locations and depths in FGM structures, even with noisy measured mode shapes and a limited amount of measured data.

Crack identification of functionally graded beam using distributed piezoelectric sensor

“…While the above-mentioned authors considered beams with material grading along the beam thickness, Lu et al (2017) developed the well-known sensitivity method for crack detection in beams with axially grading material. Zhu et al (2019) applied continuous wavelet transform for crack identification in functionally graded beams and Khiem et al (2021b) used stationary wavelet transform in combination with neural network to identify cracks in a FGM frame. In both the latter works mode shapes of FGM structures are employed for crack identification instead of natural frequencies.…”