Statistical updating of finite element model with Lamb wave sensing data for damage detection problems

Abstract Health monitoring of large structures with embedded, distributed sensor systems is gaining importance. This study proposes a new probabilistic model updating method in order to improve the damage prediction capability of a finite element analysis (FEA) model with experimental observations from a Lamb-wave sensing system. The approach statistically calibrates unknown parameters of the FEA model and estimates a bias-correcting function to achieve a good match between the model predictions and sensor observations. An experimental validation study is presented in which a set of controlled damages are generated on a composite panel. Time-series signals are collected with the damage condition using a Lamb-wave sensing system and a one dimensional FEA model of the panel is constructed to quantify the damages. The damage indices from both the experiments and the computational model are used to calibrate assumed parameters of the FEA model and to estimate a bias-correction function. The updated model is used to predict the size (extent) and location of damage. It is shown that the proposed model updating approach achieves a prediction accuracy that is superior to a purely statistical approach or a deterministic model calibration approach.

[1]  Alyson G. Wilson,et al.  Integrated Analysis of Computer and Physical Experiments , 2004, Technometrics.

[2]  Elizabeth A. Peck,et al.  Introduction to Linear Regression Analysis , 2001 .

[3]  F. Hemez,et al.  Use of response surface metamodels for identification of stiffness and damping coefficients in a simple dynamic system , 2005 .

[4]  Constantinos Soutis,et al.  Damage detection in composite materials using lamb wave methods , 2002 .

[5]  Wei Chen,et al.  A Design-Driven Validation Approach Using Bayesian Prediction Models , 2008 .

[6]  Jerome P. Lynch,et al.  A summary review of wireless sensors and sensor networks for structural health monitoring , 2006 .

[7]  Hoon Sohn,et al.  A review of structural health monitoring literature 1996-2001 , 2002 .

[8]  Peter Z. G. Qian,et al.  Bayesian Hierarchical Modeling for Integrating Low-Accuracy and High-Accuracy Experiments , 2008, Technometrics.

[9]  Thomas J. Santner,et al.  The Design and Analysis of Computer Experiments , 2003, Springer Series in Statistics.

[10]  Constantinos Soutis,et al.  Structural health monitoring techniques for aircraft composite structures , 2010 .

[11]  Sophocles J. Orfanidis,et al.  Introduction to signal processing , 1995 .

[12]  V. R. Joseph,et al.  Statistical Adjustments to Engineering Models , 2009 .

[13]  Charles R. Farrar,et al.  The fundamental axioms of structural health monitoring , 2007, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[14]  W. G. Hunter,et al.  A Useful Method For Model-Building , 1962 .

[15]  F. Chang,et al.  Detection and monitoring of hidden fatigue crack growth using a built-in piezoelectric sensor/actuator network: I. Diagnostics , 2004 .

[16]  Ying Xiong,et al.  A better understanding of model updating strategies in validating engineering models , 2009 .

[17]  P. J. Green,et al.  Probability and Statistical Inference , 1978 .

[18]  C. Fritzen,et al.  DAMAGE DETECTION BASED ON MODEL UPDATING METHODS , 1998 .

[19]  James O. Berger,et al.  A Framework for Validation of Computer Models , 2007, Technometrics.

[20]  J.P. Lynch,et al.  A Parallel Simulated Annealing Architecture for Model Updating in Wireless Sensor Networks , 2009, IEEE Sensors Journal.

[21]  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 .

[22]  A. O'Hagan,et al.  Bayesian calibration of computer models , 2001 .

[23]  Ben Wang,et al.  A minimax sensor placement approach for damage detection in composite structures , 2012 .

[24]  Carlos E. S. Cesnik,et al.  Review of guided-wave structural health monitoring , 2007 .

[25]  Douglas C. Montgomery,et al.  Applied Statistics and Probability for Engineers, Third edition , 1994 .