Detection of local defect resonance frequencies using bicoherence analysis

Abstract The nonlinear acoustic wave spectroscopy and defectometry (NAWSD) technique is found to be an efficient tool for monitoring intricate and complex defects in composite structures due to its better resolution and accuracy compared to the traditional methods. Local defect resonance (LDR) is one of such methods based on amplifying vibrations at the defect regions only, resulting in defect-selective and high resolution image. In the present work, numerical as well as experimental approach has been presented in order to detect the LDR frequency of aluminium plate having circular flat bottom hole (FBH) and delaminated glass fibre reinforced polymer composite (GFRP) plates. First, a wave propagation study using explicit dynamic analysis is performed followed by, bicoherence analysis to detect the LDR frequency numerically. The LDR frequency modes are also verified by steady state dynamic analysis. A set of experiments are then carried out using Gaussian white noise as well as chirp signal to determine the LDR frequencies from the Fast Fourier Transform (FFT) and bicoherence plots. In every cases, the bicoherence peaks corresponds to the second harmonic of LDR frequency. The bicoherence plots are found to be robust and effective tool for detection of LDR frequencies compared to FFT plots. Bicoherence analysis based LDR frequency determination technique can be further utilized in delamination and disbond imaging of composite structures.

[1]  Francesco Ciampa,et al.  Nonlinear ultrasonic stimulated thermography for damage assessment in isotropic fatigued structures , 2017 .

[2]  Steven Delrue,et al.  Detection and Characterization of Local Defect Resonances Arising from Delaminations and Flat Bottom Holes , 2017 .

[3]  Jonathan M. Nichols,et al.  Optimal bispectral detection of weak, quadratic nonlinearities in structural systems , 2010 .

[4]  Francesco Ciampa,et al.  Nonlinear air-coupled thermosonics for fatigue micro-damage detection and localisation , 2018, NDT & E International.

[5]  Cauchy Pradhan,et al.  Higher-Order Spectrum in Understanding Nonlinearity in EEG Rhythms , 2012, Comput. Math. Methods Medicine.

[6]  Gennaro Scarselli,et al.  Nonlinear imaging of damage in composite structures using sparse ultrasonic sensor arrays , 2017 .

[7]  R. Courant,et al.  On the Partial Difference Equations, of Mathematical Physics , 2015 .

[8]  Igor Solodov,et al.  A new mode of acoustic NDT via resonant air-coupled emission , 2017 .

[9]  G. Busse,et al.  A local defect resonance to enhance acoustic wave-defect interaction in ultrasonic nondestructive evaluation , 2011 .

[10]  Saeid Hedayatrasa,et al.  Towards in-plane local defect resonance for non-destructive testing of polymers and composites , 2018, NDT & E International.

[11]  Mark F. Hamilton,et al.  Finite-amplitude waves in isotropic elastic plates , 2003 .

[12]  E. Powers,et al.  Digital Bispectral Analysis and Its Applications to Nonlinear Wave Interactions , 1979, IEEE Transactions on Plasma Science.

[13]  Francesco Ciampa,et al.  Imaging of barely visible impact damage on a composite panel using nonlinear wave modulation thermography , 2018 .

[14]  Gui-Rong Liu,et al.  A step-by-step method of rule-of-mixture of fiber- and particle-reinforced composite materials , 1997 .

[15]  Igor Solodov,et al.  Resonant ultrasound spectroscopy of defects: Case study of flat-bottomed holes , 2013 .

[16]  Paul R. White,et al.  BISPECTRAL ANALYSIS OF THE BILINEAR OSCILLATOR WITH APPLICATION TO THE DETECTION OF FATIGUE CRACKS , 1998 .

[17]  Marc Kreutzbruck,et al.  Fourier-transform vibrothermography with frequency sweep excitation utilizing local defect resonances , 2017 .

[18]  P. Wilcox,et al.  Global crack detection using bispectral analysis , 2006, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[19]  Igor Solodov,et al.  Noncontact Sonic NDE and Defect Imaging Via Local Defect Resonance , 2016 .

[20]  Paul R. White,et al.  The interpretation of the bispectra of vibration signals—: I. Theory , 1995 .

[21]  Chrysostomos L. Nikias,et al.  Bispectrum estimation: A parametric approach , 1985, IEEE Trans. Acoust. Speech Signal Process..