Statistical model‐based damage localization: A combined subspace‐based and substructuring approach

This paper addresses the damage localization problem with a statistical model-based approach applied to vibration-based measurements. Damages are viewed as changes in modal parameters. Damage detection is achieved with a subspace-based residual and a global test, which performs a sensitivity analysis of the residual to the modal parameters, relative to uncertainties in those parameters and noises on the data. Damage localization is achieved by plugging the sensitivities of the modal parameters with respect to structural (finite element model) parameters in this decision framework. For large structures that have thousands of elements, a statistical substructuring method, in which the columns of the latter sensitivity matrix are clustered into different classes, is employed. This paper investigates further the clustering step. Numerical results obtained on the finite element model of a bridge deck with a large number of elements are reported. Copyright © 2007 John Wiley & Sons, Ltd.

[1]  Robert D. Adams,et al.  The location of defects in structures from measurements of natural frequencies , 1979 .

[2]  D. Zimmerman,et al.  Structural damage detection using a minimum rank update theory , 1994 .

[3]  Maurice Goursat,et al.  Output-Only Subspace-Based Structural Identification: From Theory to Industrial Testing Practice , 2001 .

[4]  Michèle Basseville,et al.  On-board Component Fault Detection and Isolation Using the Statistical Local Approach , 1998, Autom..

[5]  M. Goursat,et al.  Online Vibration-Based Crack Detection during Fatigue Testing , 2003 .

[6]  David A. Nix,et al.  Vibration–based structural damage identification , 2001, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[7]  François M. Hemez,et al.  Selection of experimental modal data sets for damage detection via model update , 1993 .

[8]  Keith Worden,et al.  Damage identification using support vector machines , 2001 .

[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]  A. Rotem,et al.  Determination of Reinforcement Unbonding of Composites by a Vibration Technique , 1969 .

[11]  Michèle Basseville,et al.  Subspace-based fault detection algorithms for vibration monitoring , 2000, Autom..

[12]  Maurice Goursat,et al.  STOCHASTIC SUBSPACE-BASED STRUCTURAL IDENTIFICATION AND DAMAGE DETECTION AND LOCALISATION—APPLICATION TO THE Z24 BRIDGE BENCHMARK , 2003 .

[13]  J. M. Ko,et al.  Damage Detection in Steel Framed Structures by Vibration Measurement Approach , 2001 .

[14]  Guido De Roeck,et al.  REFERENCE-BASED STOCHASTIC SUBSPACE IDENTIFICATION FOR OUTPUT-ONLY MODAL ANALYSIS , 1999 .

[15]  Prabhat Hajela,et al.  Damage Detection in Structures Based on Feature‐Sensitive Neural Networks , 1994 .

[16]  Hoon Sohn,et al.  DAMAGE DIAGNOSIS USING EXPERIMENTAL RITZ VECTORS , 2001 .

[17]  Seamus D. Garvey,et al.  A COMBINED GENETIC AND EIGENSENSITIVITY ALGORITHM FOR THE LOCATION OF DAMAGE IN STRUCTURES , 1998 .

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

[19]  Laurent Mevel,et al.  Health monitoring and detection of a fatigue problem of a sports car , 1999 .

[20]  Maurice Goursat,et al.  Subspace-Based Algorithms for Structural Identification, Damage Detection, and Sensor Data Fusion , 2007, EURASIP J. Adv. Signal Process..

[21]  Anne Teughels,et al.  Damage Assessment of the Z24 Bridge by FE Model Updating , 2003 .

[22]  Guido De Roeck,et al.  Damage assessment by FE model updating using damage functions , 2002 .

[23]  Michèle Basseville,et al.  Output-Only Subspace-Based Damage Detection - Application to a Reticular Structure , 2003 .

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

[25]  Norris Stubbs,et al.  Non-destructive damage evaluation of a structure from limited modal parameters , 1995 .

[26]  Poyu Tsou,et al.  Structural damage detection and identification using neural networks , 1993 .

[27]  Etienne Balmes,et al.  Iterative computation of modal sensitivities , 2006 .

[28]  Béatrice Devauchelle-Gach.  Diagnostic mécanique des fatigues sur les structures soumises à des vibrations en ambiance de travail , 1991 .

[29]  Bart Peeters,et al.  PARAMETERIZATION OF DAMAGE IN REINFORCED CONCRETE STRUCTURES USING MODEL UPDATING , 1999 .

[30]  L. Mevel,et al.  Statistical model-based damage detection and localization: subspace-based residuals and damage-to-noise sensitivity ratios , 2004 .

[31]  James H. Garrett,et al.  Use of neural networks in detection of structural damage , 1992 .

[32]  Maurice Goursat,et al.  Subspace-based modal identification and monitoring of large structures: a scilab toolbox , 2003 .

[33]  Maurice Goursat,et al.  Statistical Model-Based Damage Localization: Implementation with the Scilab Toolbox COSMAD , 2006 .

[34]  Maurice Goursat,et al.  STOCHASTIC SUBSPACE-BASED STRUCTURAL IDENTIFICATION AND DAMAGE DETECTION —APPLICATION TO THE STEEL-QUAKE BENCHMARK , 2003 .

[35]  David C. Zimmerman,et al.  Structural damage detection using minimum rank update theory and parameter estimation , 1996 .

[36]  Laurent Mevel,et al.  APPLICATION OF A SUBSPACE-BASED FAULT DETECTION METHOD TO INDUSTRIAL STRUCTURES , 1999 .

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

[38]  George C. Yao,et al.  Damage diagnosis of steel frames using vibrational signature analysis , 1992 .

[39]  Michèle Basseville,et al.  Assessment of subspace fault detection algorithms on a realistic simulator-based example , 1999 .

[40]  D. Bernal.  Load Vectors for Damage Localization , 2002 .