Crack identification with parametric optimization of entropy & wavelet transformation

Abstract

A cantilever beam with a breathing crack is studied to improve the breathing crack identification sensitivity by the parametric optimization of sample entropy and wavelet transformation. Crack breathing is a special bi-linear phenomenon experienced by fatigue cracks which are under dynamic loadings. Entropy is a measure, which can quantify the complexity or irregularity in system dynamics, and hence employed to quantify the bi-linearity/irregularity of the vibration response, which is induced by the breathing phenomenon of a fatigue crack. To improve the sensitivity of entropy measurement for crack identification, wavelet transformation is merged with entropy. The crack identification is studied under different sinusoidal excitation frequencies of the cantilever beam. It is found that, for the excitation frequencies close to the first modal frequency of the beam structure, the method is capable of detecting only 22% of the crack depth percentage ratio with respect to the thickness of the beam. Using parametric optimization of sample entropy and wavelet transformation, this crack identification sensitivity is improved up to 8%. The experimental studies are carried out, and experimental results successfully validate the numerical parametric optimization process.