Wavelet Transform for Structural Health Monitoring: A Compendium of Uses and Features

The strategic and monetary value of the civil infrastructure worldwide necessitates the development of structural health monitoring (SHM) systems that can accurately monitor structural response due to real-time loading conditions, detect damage in the structure, and report the location and nature of this damage. In the last decade, extensive research has been carried out for developing vibration-based damage detection algorithms that can relate structural dynamics changes to damage occurrence in a structure. In the mean time, the wavelet transform (WT), a signal processing technique based on a windowing approach of dilated ‘scaled’ and shifted wavelets, is being applied to a broad range of engineering applications. Wavelet transform has proven its ability to overcome many of the limitations of the widely used Fourier transform (FT); hence, it has gained popularity as an efficient means of signal processing in SHM systems. This increasing interest in WT for SHM in diverse applications motivates the authors to write an exposition on the current WT technologies. This article presents a utilitarian view of WT and its technologies. By reviewing the state-of-the-art in WT for SHM, the article discusses specific needs of SHM addressed by WT, classifies WT for damage detection into various fields, and describes features unique to WT that lends itself to SHM. The ultimate intent of this article is to provide the readers with a background on the various aspects of WT that might appeal to their need and sector of interest in SHM. Additionally, the comprehensive literature review that comprises this study will provide the interested reader a focused search to investigate using wavelets in SHM.

[1]  Ahsan Kareem,et al.  Applications of wavelet transforms in earthquake, wind and ocean engineering , 1999 .

[2]  Norris Stubbs,et al.  Damage identification in beam-type structures: frequency-based method vs mode-shape-based method , 2003 .

[3]  Ibrahim Esat,et al.  ARTIFICIAL NEURAL NETWORK BASED FAULT DIAGNOSTICS OF ROTATING MACHINERY USING WAVELET TRANSFORMS AS A PREPROCESSOR , 1997 .

[4]  Hani G. Melhem,et al.  DAMAGE DETECTION IN CONCRETE BY FOURIER AND WAVELET ANALYSES , 2003 .

[5]  Lin Ye,et al.  Lamb wave-based quantitative identification of delamination in CF/EP composite structures using artificial neural algorithm , 2004 .

[6]  S. Mallat A wavelet tour of signal processing , 1998 .

[7]  Paolo Venini,et al.  Wavelet Analysis of Structures: Statics, Dynamics and Damage Identification , 1997 .

[8]  Luis E. Suarez,et al.  Applications of wavelet transforms to damage detection in frame structures , 2004 .

[9]  Tshilidzi Marwala,et al.  DAMAGE IDENTIFICATION USING COMMITTEE OF NEURAL NETWORKS , 2000 .

[10]  Akira Sone,et al.  Health monitoring system of structures based on orthonormal wavelet transform , 1995 .

[11]  Shih-Fu Ling,et al.  Machinery Diagnosis Based on Wavelet Packets , 1997 .

[12]  Alan S. Willsky,et al.  A Wavelet Packet Approach to Transient Signal Classification , 1995 .

[13]  A. Al-Khalidy,et al.  Ball bearing early fault detection using wavelet analysis , 2003 .

[14]  A. F. Stronach,et al.  The Application of Advanced Signal Processing Techniques to Induction Motor Bearing Condition Diagnosis , 2003 .

[15]  P. D. McFadden,et al.  A review of time-frequency methods for structural vibration analysis , 2003 .

[16]  George Vachtsevanos,et al.  Fault prognosis using dynamic wavelet neural networks , 2001, 2001 IEEE Autotestcon Proceedings. IEEE Systems Readiness Technology Conference. (Cat. No.01CH37237).

[17]  S. Patsias,et al.  Damage Detection Using Optical Measurements and Wavelets , 2002 .

[18]  Yong. Xia,et al.  Condition assessment of structures using dynamic data. , 2002 .

[19]  Gang Qi,et al.  Discrete wavelet decomposition of acoustic emission signals from carbon-fiber-reinforced composites , 1997 .

[20]  Surendra P. Shah,et al.  Assessing Damage in Corroded Reinforced Concrete Using Acoustic Emission , 2000 .

[21]  R. Christensen,et al.  Multivariate Statistical Modelling , 1987 .

[22]  Sagar V. Kamarthi,et al.  Feature Extraction From Wavelet Coefficients for Pattern Recognition Tasks , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Bruno Torrésani,et al.  Characterization of signals by the ridges of their wavelet transforms , 1997, IEEE Trans. Signal Process..

[25]  Ronald R. Coifman,et al.  Entropy-based algorithms for best basis selection , 1992, IEEE Trans. Inf. Theory.

[26]  Emine Ayaz,et al.  Feature extraction related to bearing damage in electric motors by wavelet analysis , 2003, J. Frankl. Inst..

[27]  Lin Ye,et al.  Digital Damage Fingerprints (DDF) and its application in quantitative damage identification , 2005 .

[28]  Naser El-Sheimy,et al.  Structural health monitoring using the semantic wireless: a novel application for wireless networking , 2002, 27th Annual IEEE Conference on Local Computer Networks, 2002. Proceedings. LCN 2002..

[29]  W. J. Wang,et al.  Application of orthogonal wavelets to early gear damage detection , 1995 .

[30]  Christos Yiakopoulos,et al.  Wavelet Based Demodulation of Vibration Signals Generated by Defects in Rolling Element Bearings , 2002 .

[31]  R. Ogden,et al.  Essential Wavelets for Statistical Applications and Data Analysis , 1996 .

[32]  Keith Hjelmstad,et al.  STRUCTURAL DAMAGE DETECTION AND ASSESSMENT FROM MODAL RESPONSE , 2003 .

[33]  Chris Rizos,et al.  Principal Component Analysis of Wavelet Transformed GPS Data for Deformation Monitoring , 2002 .

[34]  Y. Kim,et al.  Damage detection using the Lipschitz exponent estimated by the wavelet transform: applications to vibration modes of a beam , 2002 .

[35]  Alexander G. Parlos,et al.  Induction motor fault diagnosis based on neuropredictors and wavelet signal processing , 2002 .

[36]  Rune Brincker,et al.  Vibration Based Inspection of Civil Engineering Structures , 1993 .

[37]  C. Chui Wavelets: A Tutorial in Theory and Applications , 1992 .

[38]  Chih-Chen Chang,et al.  Structural Damage Assessment Based on Wavelet Packet Transform , 2002 .

[39]  S. R. Green,et al.  Structural damage detection using the holder exponent. , 2002 .

[40]  V. Alarcón-Aquino,et al.  Anomaly detection in communication networks using wavelets , 2001 .

[41]  Zhikun Hou,et al.  Application of Wavelet Approach for ASCE Structural Health Monitoring Benchmark Studies , 2004 .

[42]  M. Farid Golnaraghi,et al.  A neuro-fuzzy approach to gear system monitoring , 2004, IEEE Transactions on Fuzzy Systems.

[43]  Saeed Shiry,et al.  Visual feature extraction via PCA-based parameterization of wavelet density functions , 2002 .

[44]  James S. Walker,et al.  A Primer on Wavelets and Their Scientific Applications , 1999 .

[45]  Gerald Kaiser,et al.  A Friendly Guide to Wavelets , 1994 .

[46]  Charles R. Farrar,et al.  A summary review of vibration-based damage identification methods , 1998 .

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

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

[49]  W. J. Staszewski,et al.  Structural and Mechanical Damage Detection Using Wavelets , 1998 .

[50]  Aili Mari Tripp,et al.  International , 2010, PS: Political Science & Politics.

[51]  Hoon Sohn,et al.  Singularity detection for structural health monitoring using Holder exponents , 2003, SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

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

[53]  Y. Meyer Wavelets and Operators , 1993 .

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

[55]  J. S. Mayer,et al.  Wavelets and electromagnetic power system transients , 1996 .

[56]  Atsushi Iwata,et al.  Gabor-Type Filtering using Transient States of Cellular Neural Networks , 2004, Intell. Autom. Soft Comput..

[57]  Marcelo Braga dos Santos,et al.  Identification of rotary machines excitation forces using wavelet transform and neural networks , 2002 .

[58]  Mohammad Noori,et al.  Wavelet-Based Approach for Structural Damage Detection , 2000 .

[59]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[60]  Laurence Tianruo Yang,et al.  Fuzzy Logic with Engineering Applications , 1999 .

[61]  Victor Giurgiutiu,et al.  Review of Vibration-Based Helicopters Health and Usage Monitoring Methods , 2001 .

[62]  N. Stubbs,et al.  Damage detection on a steel frame using simulated modal data , 1998 .

[63]  Volkmar Dr.-Ing. Zabel Applications of Wavelet Analysis in System Identification , 2003 .

[64]  Ru-Shan Wu,et al.  Comparison of propagator decomposition in seismic imaging by wavelets, wavelet packets, and local harmonics , 1998, Optics & Photonics.

[65]  Sanjit K. Mitra,et al.  Digital Signal Processing: A Computer-Based Approach , 1997 .

[66]  Satish T. S. Bukkapatnam,et al.  Analysis of acoustic emission signals in machining , 1999 .

[67]  C. Burrus,et al.  Introduction to Wavelets and Wavelet Transforms: A Primer , 1997 .

[68]  Martin Furlan,et al.  Fault Detection in DC Electro Motors Using the Continuous Wavelet Transform , 2003 .

[69]  L. Yam,et al.  Detection of delamination damage in composite plates using energy spectrum of structural dynamic responses decomposed by wavelet analysis , 2004 .

[70]  Multiresolution Analysis of Elastic Degradation in Heterogeneous Materials , 2001 .

[71]  L. H. Yam,et al.  Vibration-based damage detection for composite structures using wavelet transform and neural network identification , 2003 .

[72]  Keith Worden,et al.  EXPERIMENTAL VALIDATION OF A STRUCTURAL HEALTH MONITORING METHODOLOGY: PART II. NOVELTY DETECTION ON A GNAT AIRCRAFT , 2003 .

[73]  Stéphane Mallat,et al.  Singularity detection and processing with wavelets , 1992, IEEE Trans. Inf. Theory.

[74]  M. Haase,et al.  Damage identification based on ridges and maxima lines of the wavelet transform , 2003 .

[75]  Guido De Roeck,et al.  STRUCTURAL DAMAGE IDENTIFICATION USING MODAL DATA. I: SIMULATION VERIFICATION , 2002 .

[76]  Barbara Hubbard,et al.  The World According to Wavelets , 1996 .

[77]  I. Daubechies Orthonormal bases of compactly supported wavelets , 1988 .

[78]  Pc Pandey,et al.  Vibration signature analysis using artificial neural networks , 1995 .

[79]  Stanley L. Sclove,et al.  Multivariate statistical modeling , 1994 .

[80]  Mac A. Cody The fast wavelet transform , 1992 .

[81]  Asim Karim,et al.  Fast Automatic Incident Detection on Urban and Rural Freeways Using Wavelet Energy Algorithm , 2003 .

[82]  A. Kareem,et al.  First- and Higher-Order Correlation Detection Using Wavelet Transforms , 2003 .

[83]  R. Ruotolo,et al.  Crack detection of a beam using the Wavelet Transform , 1994 .

[84]  Naser El-Sheimy,et al.  A neuro-wavelet method for multi-sensor system integration for vehicular navigation , 2004 .

[85]  M. M. Reda Taha,et al.  Damage identification for structural health monitoring using fuzzy pattern recognition , 2005 .

[86]  S. Grondel,et al.  Damage assessment in composites by Lamb waves and wavelet coefficients , 2003 .

[87]  Qiang Liu,et al.  Fuzzy pattern recognition of AE signals for grinding burn , 2005 .

[88]  Michael T. Orchard,et al.  Wavelet packet image coding using space-frequency quantization , 1998, IEEE Trans. Image Process..

[89]  Nicholas A J Lieven,et al.  Damage Prognosis: Current Status and Future Needs , 2003 .

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

[91]  T. Christaller,et al.  Wavelet Entropy-based Feature Extraction for Crack Detection in Sewer Pipes , 2002 .

[92]  Y. Ueno,et al.  Prediction of spalling on a ball bearing by applying the discrete wavelet transform to vibration signals , 1996 .

[93]  David Jerison The World According to Wavelets : The Story of a Mathematical Technique in the Making Reviewed by David Jerison , 1999 .

[94]  Christophe Paget Active Health Monitoring of Aerospace Composite Structures by Embedded Piezoceramic Transducers , 2001 .

[95]  Jinsong Zhao,et al.  Multidimensional non-orthogonal wavelet-sigmoid basis function neural network for dynamic process fault diagnosis , 1998 .

[96]  A wavelet‐like Galerkin method for numerical solution of variational inequalities arising in elastoplasticity , 2000 .

[97]  Bernard Dubuisson,et al.  Engine Knock Detection from Vibration Signals Using Pattern Recognition , 1997 .

[98]  Sujit K. Ghosh,et al.  Essential Wavelets for Statistical Applications and Data Analysis , 2001, Technometrics.

[99]  Naser El-Sheimy,et al.  Introduction to the use of wavelet multiresolution analysis for intelligent structural health monitoring , 2004 .

[100]  Chih-Chieh Chang,et al.  Damage detection of cracked thick rotating blades by a spatial wavelet based approach , 2004 .

[101]  Keith Worden,et al.  An Overview of Intelligent Fault Detection in Systems and Structures , 2004 .

[102]  T. Ross Fuzzy Logic with Engineering Applications , 1994 .

[103]  K. M. Liew,et al.  DETECTION OF DAMAGE LOCATIONS IN A BEAM USING THE WAVELET ANALYSIS , 2001 .

[104]  G. Tomlinson,et al.  Wavelet signal processing for enhanced Lamb-wave defect detection in composite plates using optical fiber detection , 1997 .

[105]  Umberto Meneghetti,et al.  Application of the envelope and wavelet transform analyses for the diagnosis of incipient faults in ball bearings , 2001 .

[106]  Deh-Shiu Hsu,et al.  Diagnosis of Reinforced Concrete Structural Damage Base on Displacement Time History Using the Back-Propagation Neural Network Technique , 2002 .

[107]  Guillermo A. Jaquenod,et al.  Digital Signal Processing, A Computer Based Approach . 2nd Edition , 2003 .

[108]  Ahsan Kareem,et al.  WAVELET TRANSFORMS FOR SYSTEM IDENTIFICATION AND ASSOCIATED PROCESSING CONCERNS , 2002 .

[109]  Chih-Chen Chang,et al.  Structural Degradation Monitoring using Covariance-driven Wavelet Packet Signature , 2003 .

[110]  Soundar Kumara,et al.  Machinery Fault Diagnosis and Prognosis: Application of Advanced Signal Processing Techniques , 1999 .