A Wavelet Packet Based Sifting Process and its Application for Structural Health Monitoring

This article presents an innovative wavelet packet based sifting process to decompose a signal into its components with different frequency contents by examining the energy content in the wavelet packet components of a signal and imposing certain decomposition criteria. A new method is illustrated for simulation data of a linear three-degree-of-freedom spring-mass-damper system and the results are compared with those obtained using the empirical mode decomposition (EMD) method. Both methods provide good approximations, as compared with the exact solution for modal responses from a conventional modal analysis and both show relatively greater errors at the beginning and ending parts of the signal due to the well-known end effects. A comparison study is also provided to illustrate differences between two sifting processes of the proposed approach and the EMD, by using a harmonic signal with a sweeping frequency and the impulse response of a linear single-degree-of-freedom system with viscous damping. Incorporated with the classical Hilbert transform, the proposed sifting process may be effectively used for structural health monitoring, including both detecting abrupt loss of structural stiffness and monitoring development of progressive stiffness degradation, as demonstrated by two case studies. Results from a preliminary study for experimental data from a shaking table test of a full-size two-story wooden building structure are also presented to show great promise of the proposed method in practical applications of health monitoring of real structures.

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

[2]  James L. Beck,et al.  Two-Stage Structural Health Monitoring Approach for Phase I Benchmark Studies , 2004 .

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

[4]  Gabriel Rilling,et al.  Empirical mode decomposition as a filter bank , 2004, IEEE Signal Processing Letters.

[5]  Lambros S. Katafygiotis,et al.  Probabilistic approach for modal identification using non‐stationary noisy response measurements only , 2002 .

[6]  Yu Lei,et al.  Hilbert-Huang Based Approach for Structural Damage Detection , 2004 .

[7]  SuzanneWeaver Smith,et al.  Model Correlation and Damage Location for Large Space Truss Structures , 1991 .

[8]  Ian Howard,et al.  Vibration fault detection of large turbogenerators using neural networks , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[9]  Walter M. West,et al.  Illustration of the use of modal assurance criterion to detect structural changes in an Orbiter test specimen , 1986 .

[10]  M. R. Dellomo.  Helicopter Gearbox Fault Detection: A Neural Network Based Approach , 1999 .

[11]  Fereidoun Amini,et al.  Neural Network for Structure Control , 1995 .

[12]  Biswanath Samanta,et al.  Bearing Fault Detection Using Artificial Neural Networks and Genetic Algorithm , 2004, EURASIP J. Adv. Signal Process..

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

[14]  Chih-Chen Chang,et al.  Continuous condition assessment for bridges based on wavelet packets decomposition , 2001, SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[15]  J.B. Allen,et al.  A unified approach to short-time Fourier analysis and synthesis , 1977, Proceedings of the IEEE.

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

[17]  Richard D. Braatz,et al.  Fault Detection and Diagnosis in Industrial Systems , 2001 .

[18]  Lambros S. Katafygiotis,et al.  Application of a Statistical Model Updating Approach on Phase I of the IASC-ASCE Structural Health Monitoring Benchmark Study , 2004 .

[19]  Massimo Ruzzene,et al.  MODELLING AND IDENTIFICATION OF THE DYNAMIC RESPONSE OF A SUPPORTED BRIDGE , 2000 .

[20]  Kuo-Chung Lin,et al.  Wavelet packet feature extraction for vibration monitoring , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

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

[22]  Poul Henning Kirkegaard,et al.  Damage Detection in an Offshore Structure , 1995 .

[23]  O. S. Salawu.  STRUCTURAL DAMAGE OETECTION USING EXPERIMENTAL MODAL ANALYSIS A COMPARISON OF SOME METHODS , 2001 .

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

[25]  R. Ghanem,et al.  A WAVELET-BASED APPROACH FOR THE IDENTIFICATION OF LINEAR TIME-VARYING DYNAMICAL SYSTEMS , 2000 .

[26]  Gary G. Yen,et al.  Wavelet packet feature extraction for vibration monitoring , 2000, IEEE Trans. Ind. Electron..

[27]  Charles R. Farrar,et al.  Applying the LANL Statistical Pattern Recognition Paradigm for Structural Health Monitoring to Data from a Surface-Effect Fast Patrol Boat , 2001 .

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

[29]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[30]  N. Huang,et al.  System identification of linear structures based on Hilbert–Huang spectral analysis. Part 1: normal modes , 2003 .

[31]  Hojjat Adeli,et al.  Neural Networks in Civil Engineering: 1989–2000 , 2001 .

[32]  Hoon Sohn,et al.  Damage diagnosis using time series analysis of vibration signals , 2001 .

[33]  Charles R. Farrar,et al.  Structural Health Monitoring Using Statistical Pattern Recognition Techniques , 2001 .

[34]  L. Prasad,et al.  WAVELET ANALYSIS with Applications to IMAGE PROCESSING , 1997 .

[35]  Boualem Boashash,et al.  Estimating and interpreting the instantaneous frequency of a signal. I. Fundamentals , 1992, Proc. IEEE.

[36]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[37]  O. Nelles.  Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models , 2000 .

[38]  Yanchun Liang,et al.  IDENTIFICATION OF RESTORING FORCES IN NON-LINEAR VIBRATION SYSTEMS BASED ON NEURAL NETWORKS , 1997 .

[39]  Lambros S. Katafygiotis,et al.  Bayesian modal updating using complete input and incomplete response noisy measurements , 2002 .

[40]  David C. Zimmerman,et al.  Model Refinement and Damage Location for Intelligent Structures , 1992 .

[41]  Soheil Saadat.  Structural Health Monitoring and Detection of Progressive and Existing Damage using Artificial Neural Networks-Based System Identification , 2003 .

[42]  K. H. LAW,et al.  An Enhanced Statistical Damage Detection Algorithm Using Time Series Analysis , 2003 .

[43]  O. Rioul,et al.  Wavelets and signal processing , 1991, IEEE Signal Processing Magazine.

[44]  Charles R. Farrar,et al.  Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: A literature review , 1996 .

[45]  Massimo Ruzzene,et al.  NATURAL FREQUENCIES AND DAMPINGS IDENTIFICATION USING WAVELET TRANSFORM: APPLICATION TO REAL DATA , 1997 .

[46]  A. K. Pandey,et al.  Damage Detection in Structures Using Changes in Flexibility , 1994 .

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

[48]  Richard W. Longman,et al.  Identification of linear structural systems using earthquake‐induced vibration data , 1999 .

[49]  J. N. Yang,et al.  System identification of linear structures using Hilbert transform and empirical mode decomposition , 2000 .

[50]  Ahsan Kareem,et al.  ON THE PRESENCE OF END EFFECTS AND THEIR MELIORATION IN WAVELET-BASED ANALYSIS , 2002 .

[51]  Ahsan Kareem,et al.  Wavelet Transforms for System Identification in Civil Engineering , 2003 .