Detecting damage in beams through digital differentiator filters and continuous wavelet transforms

Crack identification using wavelets on experimental static deflection profiles

“…The possibility of damage detection by the Haar [10,13,18], Gabor [10,13,15], Mexican Hat [4,18], Symlet [2,11,18], Coiflet [21,18] or Gaussian wavelets [9,7,18] has been discussed. The number of vanishing moments and the possibility of computing of the discrete wavelet transforms of the above wavelets are given in Table 1.…”

“…However, the use of a Gaussian wavelet guarantees that modulus maxima are never interrupted when the scale decreases. The advantages of the real Gaussian wavelets considered as compact differential filters have been discussed by Messina [7].…”

“…This method proved to be effective in the case of vibration data obtained from numerical simulations. However, with noisy experimental data, the success of the technique is significantly affected [7]. An improved identification method based on modal information was presented by Kim and Stabbs [8].…”

“…These small defects cannot be identified directly from the structure response, but may be observed on wavelet transforms since local abnormalities in the signal result in large wavelet coefficients in the neighbourhood of the damage. Previous studies have shown very good accuracy and effectiveness of wavelet transforms [2,4,7,[9][10][11][12][13] although most of the investigations were performed on numerical data without experimental verification. Gentile and Messina [9] as well as Hong et al [4] pointed out the importance of taking into account the effect of noise.…”

Structural Health Monitoring in Composite Automotive Elements

“…The comparison of differentiation process among Wavelets transform and several other differential operators, to calculate the curvature mode shape and sequent location of damage in beams, was presented by Messina (Messina, 2004). The differential operators integrate a low-pass filter to reduce the unwanted high frequency noise.…”