On the effect of spatial sampling in damage detection of cracked beams by continuous wavelet transform

Abstract Modern measurement techniques are improving in capability to capture spatial displacement fields occurring in deformed structures with high precision and in a quasi-continuous manner. This in turn has made the use of vibration-based damage identification methods more effective and reliable for real applications. However, practical measurement and data processing issues still present barriers to the application of these methods in identifying several types of structural damage. This paper deals with spatial Continuous Wavelet Transform (CWT) damage identification methods in beam structures with the aim of addressing the following key questions: (i) Can the cost of damage detection be reduced by down-sampling? (ii) What is the minimum number of sampling intervals required for optimal damage detection? The first three free vibration modes of a cantilever and a simple supported beam with an edge open crack are numerically simulated. A thorough parametric study is carried out by taking into account the key parameters governing the problem, including the wavelet mother function, level of noise, crack depth and location, mechanical and geometrical parameters of the beam and the padding method to reduce border distortions. The results are employed to assess the optimal number of sampling intervals for effective damage detection.

[1]  Biswajit Basu,et al.  A Statistical Measure for Wavelet Based Singularity Detection , 2009 .

[2]  Lorenzo Montanari,et al.  Vibration-based damage identification in beam structures through wavelet analysis , 2014 .

[3]  Vistasp M. Karbhari,et al.  Improved damage detection method based on Element Modal Strain Damage Index using sparse measurement , 2008 .

[4]  Pramod Rastogi,et al.  Optical measurement techniques and applications , 1997 .

[5]  Arun Kumar Pandey,et al.  Damage detection from changes in curvature mode shapes , 1991 .

[6]  S. Loutridis,et al.  CRACK IDENTIFICATION IN BEAMS USING WAVELET ANALYSIS , 2003 .

[7]  Xiaomin Deng,et al.  Damage detection with spatial wavelets , 1999 .

[8]  Stathis C. Stiros,et al.  Monitoring dynamic and quasi-static deformations of large flexible engineering structures with GPS: Accuracy, limitations and promises , 2006 .

[9]  C. Fuggini,et al.  Engineering vibration monitoring by GPS: long duration records , 2009 .

[10]  Shuncong Zhong,et al.  Sampling interval sensitivity analysis for crack detection by stationary wavelet transform , 2013 .

[11]  Gangbing Song,et al.  Recent applications of fiber optic sensors to health monitoring in civil engineering , 2004 .

[12]  Cecilia Surace,et al.  Structural Damage Detection Using the Polynomial Annihilation Edge Detection Method , 2014 .

[13]  Edward Sazonov,et al.  Optimal spatial sampling interval for damage detection by curvature or strain energy mode shapes , 2005 .

[14]  Daniel Rixen,et al.  Feasibility of monitoring large wind turbines using photogrammetry , 2010 .

[15]  Jean-José Orteu,et al.  3-D computer vision in experimental mechanics , 2007 .

[16]  M. Allen,et al.  Output-Only Modal Analysis Using Continuous-Scan Laser Doppler Vibrometry and Application to a 20 kW Wind Turbine , 2010 .

[17]  Vikram Pakrashi,et al.  Structural damage detection and calibration using a wavelet-kurtosis technique , 2007 .

[18]  S. Loutridis,et al.  Crack identification in double-cracked beams using wavelet analysis , 2004 .

[19]  Krzysztof Wilde,et al.  Application of continuous wavelet transform in vibration based damage detection method for beams and plates , 2006 .

[20]  Shuncong Zhong,et al.  Detection of cracks in simply-supported beams by continuous wavelet transform of reconstructed modal data , 2011 .

[21]  Li Cheng,et al.  Development in vibration-based structural damage detection technique , 2007 .

[22]  A. Messina,et al.  On the continuous wavelet transforms applied to discrete vibrational data for detecting open cracks in damaged beams , 2003 .

[23]  K. M. Liew,et al.  Application of Wavelet Theory for Crack Identification in Structures , 1998 .

[24]  H. Langhaar.  Dimensional analysis and theory of models , 1951 .

[25]  Biswajit Basu,et al.  A padding method to reduce edge effects for enhanced damage identification using wavelet analysis , 2015 .

[26]  Zhishen Wu,et al.  Assessment of Vibration-based Damage Identification Methods Using Displacement and Distributed Strain Measurements , 2009 .

[27]  Cecilia Surace,et al.  On the use of the polynomial annihilation edge detection for locating cracks in beam-like structures , 2013 .

[28]  Fabio Casciati,et al.  Investigating the Potential of LPS in Structural Health Monitoring , 2010, 2010 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM).

[29]  A. Messina.  Refinements of damage detection methods based on wavelet analysis of dynamical shapes , 2008 .

[30]  A. Messina.  Detecting damage in beams through digital differentiator filters and continuous wavelet transforms , 2004 .

[31]  David J. Ewins,et al.  MODAL TESTING USING A SCANNING LASER DOPPLER VIBROMETER , 1999 .

[32]  Quan Wang,et al.  Detecting the delamination location of a beam with a wavelet transform: an experimental study , 2010 .