Vibration-based damage detection in plates by using time series analysis

Abstract This paper deals with the problem of vibration health monitoring (VHM) in structures with nonlinear dynamic behaviour. It aims to introduce two viable VHM methods that use large amplitude vibrations and are based on nonlinear time series analysis. The methods suggested explore some changes in the state space geometry/distribution of the structural dynamic response with damage and their use for damage detection purposes. One of the methods uses the statistical distribution of state space points on the attractor of a vibrating structure, while the other one is based on the Poincare map of the state space projected dynamic response. In this paper both methods are developed and demonstrated for a thin vibrating plate. The investigation is based on finite element modelling of the plate vibration response. The results obtained demonstrate the influence of damage on the local dynamic attractor of the plate state space and the applicability of the proposed strategies for damage assessment. The approach taken in this study and the suggested VHM methods are rather generic and permit development and applications for other more complex nonlinear structures.

[1]  Chistian Boller,et al.  An investigation on vibration-based damage diagnosis in thin plates , 2004 .

[2]  H. Kantz,et al.  Nonlinear time series analysis , 1997 .

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

[4]  W. Ostachowicz,et al.  On Approximate Analytical Solutions for Vibrations in Cracked Plates , 2006 .

[5]  J. R. Hutchinson Response of a free circular plate to a central transverse load , 1988 .

[6]  Mehmet Emre Çek,et al.  Analysis of observed chaotic data , 2004 .

[7]  E. Manoach Dynamic large deflection analysis of elastic-plastic Mindlin circular plates , 1994 .

[8]  Inderjit Chopra,et al.  Wind tunnel testing of a Mach-scaled rotor model with trailing-edge flaps , 2000 .

[9]  Fraser,et al.  Independent coordinates for strange attractors from mutual information. , 1986, Physical review. A, General physics.

[10]  J. Nichols,et al.  Damage detection using multivariate recurrence quantification analysis , 2006 .

[11]  Pedro Ribeiro,et al.  Coupled, thermoelastic, large amplitude vibrations of Timoshenko beams , 2004 .

[12]  Joseph Mathew Some recent advances in signal processing for vibration monitoring , 1997 .

[13]  Irina Trendafilova,et al.  Vibration-based damage detection in structures using time series analysis , 2006 .

[14]  Irina Trendafilova,et al.  State Space Modelling and Representation for Vibration-Based Damaged Assessment , 2003 .

[15]  Mehmet Imregun,et al.  STRUCTURAL DAMAGE DETECTION USING ARTIFICIAL NEURAL NETWORKS AND MEASURED FRF DATA REDUCED VIA PRINCIPAL COMPONENT PROJECTION , 2001 .

[16]  L. Pecora,et al.  Vibration-based damage assessment utilizing state space geometry changes: local attractor variance ratio , 2001 .

[17]  Michael D. Todd,et al.  A multivariate, attractor-based approach to structural health monitoring , 2005 .

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

[19]  K. Mardia Measures of multivariate skewness and kurtosis with applications , 1970 .

[20]  S. E. Khadem,et al.  INTRODUCTION OF MODIFIED COMPARISON FUNCTIONS FOR VIBRATION ANALYSIS OF A RECTANGULAR CRACKED PLATE , 2000 .

[21]  Daniel J. Inman,et al.  TIME DOMAIN ANALYSIS FOR DAMAGE DETECTION IN SMART STRUCTURES , 1997 .