Journals and Conferences Dealing with Health Monitoring



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Health Monitoring 1/29/2017 © D. Adams 2006

Appendix B


  1. Journals and Conferences Dealing with Health Monitoring

Table B.1 and Table B.2 in this section provide lists of technical Journals and conferences that highlight developments in health monitoring. These tables will be updated as necessary to provide up-to-date information





  1. Sensors

In -Table B.10, different types of displacement, velocity, acceleration, strain, force, temperature, and pressure sensors are summarized.





  1. References on Data Analysis from the Literature

In Table B.11-Table B.18, references from the literature on a wide range of data analysis topics in health monitoring are summarized and cited. These references will be updated as necessary to provide up-to-date information.


Table B.1 – Technical Journals in health monitoring.



Journal Name

Publisher

AIAA Journal

American Institute of Aeronautics and Astronautics

Experimental Mechanics

Society of Experimental Mechanics

International Journal of Analytical and Experimental Modal Analysis

CSA Illumina

International Journal of Engineering Science

CSA Illumina

International Journal of Fatigue

Elsevier Science

International Journal of Fracture

Springer

Journal of Applied Mechanics

American Society of Mechanical Engineers

Journal of Dynamic Systems, Measurement, and Control

American Society of Mechanical Engineers

Journal of Engineering Mechanics

American Society of Civil Engineers

Journal of Intelligent Material Systems and Structures

Sage Publishers

Journal of Pressure Vessel Technology

American Society of Mechanical Engineers

Journal of Sound and Vibration

Academic Press

Journal of Structural Engineering

American Society of Civil Engineers

Journal of Vibration and Acoustics

American Society of Mechanical Engineers

Mechanical Systems and Signal Processing

Academic Press

NDT&E International

Elsevier Science

Physical Review Letters

American Physical Society

Sensors Actuators

CSA Illumina

Smart Materials and Structures

Institute of Physics

Structural Health Monitoring: An International Journal

Sage Publishers

The Journal of the Acoustical Society of America

Acoustical Society of America

The Shock and Vibration Digest

Sage Publishers

Table B.2 – Technical conferences in health monitoring.



Conference Name

International Modal Analysis Conference

European Workshop on Structural Health Monitoring

International Workshop on Structural Health Monitoring

The International Society for Optical Engineering (SPIE)

International Mechanical Engineering Congress

Asia-Pacific Conference on Systems Integrity and Maintenance (ACSIM)

IEEE Aerospace Conference

International Conference on Adaptive Structures and Technologies

AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

International Conference on Adaptive Structures

IEEE Conference on Antennas and Propagation

International Conference on Damage Assessment of Structures

International Design Engineering Technical Conference

Society for the Advancement of Material and Process Engineering Conference

Integrated Systems Health Management Conference

Health and Usage Monitoring Conference

Machinery Failure Prevention Technology Annual Meeting

Materials Science and Technology Conference

Quantitative NDE Conference

AIAA/ASME/ASCE/ASC Structures, Structural Dynamics & Materials Conference


Table B.3 – Displacement sensors.



Table B.4 – Velocity sensors.



Table B.5 – Acceleration sensors.



Table B.6 – Strain sensors.



Table B.7 – Force sensors.



Table B.8 – Temperature sensors.



Table B.9 – Pressure sensors.


Table B.10 – Piezoelectric actuators.



Table B.11 – References on methods for loads identification.

Reference

Summary

Stevens, K.K., 1987, “Force Identification Problems-An Overview”

Conference: Overview of indirect force estimation for linear systems.

Chae et al., 1999, “A Feasibility Study in Indirect Identification of Transmission Forces through Rubber Bushing in Vehicle Suspension System by Using Vibration Signals Measured on Links”

Journal: Relates the transmission force to the deformation of rubber bushings through an appropriate model.

Decker, M. and Savaidis, G., 2002, “Measurement and Analysis of Wheel Loads for Design and Fatigue Evaluation of Vehicle Chassis Components”

Journal: Discussed the interactions of wheel forces and moments, forces acting in a suspension, and the stress response of an axle casing.

O’Connor, C., and Chan, T.H.T., 1988, “Dynamic Wheel loads From Bridge Strains”

Journal: Modeled the bridge deck as lumped masses interconnected by mass-less elastic beams and estimated loading of bridge due to wheels.

Chan, T.H.T., Law, S.S., Yung, T.H. and Yuan, X.R., 1999, “An Interpretive Method for Moving Force Identification”

Journal: Modeled the bridge deck using Bernoulli-Euler beams and estimated loading of bridge due to wheels.

Zhu, X.Q. and Law, S.S., 2000, “Identification of Vehicle Axle Loads from Bridge Responses”

Journal: Modeled the bridge deck as orthotropic plates and estimated loading of bridge due to wheels.

Wang, M.L. and Kreitinger, T.J., 1994, “Identification of Force from Response Data of a Nonlinear System”

Journal: Presented the sum of weighted acceleration technique (SWAT) to estimate the input force.

Giergil, J. and Uhl, T., 1989, “Identification of the Input excitation forces in mechanical structures”


Journal: Presented an iterative formula for calculation of excitation forces in mechanical structures based on properties of the Toeplitz matrix.

Haas, D.J., Milano and Flitter, L., 1995, “Prediction of Helicopter Component Loads Using Neural Networks”


Journal: Used a neural network approach to relate rotor system component loads to flight data recorded using a flight recorder.

Giasante et al., 1983, “Determination of In-Flight Helicopter Loads”

Journal: Identified the external vibratory forces acting on a helicopter in flight using a calibration matrix.

Li, J., 1988, “Application of Mutual Energy Theorem for Determining Unknown Force Sources”


Conference: Identified spectrum of loads based on vibration velocity response measurements.

Zion, L., 1994, “Predicting Fatigue Loads Using Regression Diagnostics”


Conference: Presented an approach based on a regression model relating loads and flight data in a helicopter.

Uhl, T. and Pieczara, J., 2003, “Identification of Operational Loading Forces for Mechanical Structures”


Journal: Based on the difference between measured and simulated system responses, genetic algorithm estimates loads.

Starkey, J.M., and G.L. Merrill, 1989, “On the Ill-Conditioned Nature of Indirect Force-Measurement Techniques”

Journal: Investigated the ill-conditioned nature of the inverse problem and found that the condition of the FRF matrix is a good indicator of errors.

Bartlett, F.D., Jr., and W.G. Flannelly, 1979, “Model Verification of Force Determination for Measuring Vibratory Loads”

Journal: Found that the pseudo-inverse method of force estimation worked well for identifying vibrations forces on the rotary hub of a helicopter model

Hundhausen, R.J., D.E. Adams, M. Derriso, Kukuchek, P., and Alloway, R., 2005, “Transient Loads Identification for a Standoff Metallic Thermal Protection System Panel”

Conference: Used two methods for identifying transient loads on standoff metallic panels: 1) rigid body approach, and 2) inverse FRF approach.

Turco, E., 2005, “A Strategy to Identify Exciting Forces Acting on Structures”

Journal: Explores the use of the Tikhonov regularization technique to reduce ill-conditioning effects of frequency domain equations for pin-jointed trusses.

Kammer, D.C., 1996, “Input Force Reconstruction Using a Time Domain Technique”

Journal: Convolves the measured response and an inverse system of Markov parameters to estimate input forces on a structure in the time domain.

Jacquelin, E., Bennani, A., and Hamelin, P, 2003, “Force Reconstruction: Analysis and Regularization of a Deconvolution Problem”

Journal: Applies Tikhonov and trunctation regularization techniques to the indirect force estimation problem and chooses the regularization parameters.

Fabunmi, J.A., 1986, “Effects of Structural Modes on Vibratory Force Determination by the Pseudoinverse Technique”

Journal: Studied the implication of using the least-squares method of force identification without considering the modes and mode shapes.

Carne, T.G., Mayes, R.L., and Bateman, V.I., 1994, “Force Reconstruction Using the Sum of Weighted Acceleration Technique—Max-Flat Procedure”

Conference: Used FRF data to determine appropriate scalar weights to use in the Sum of Weighted Acceleration Technique for force reconstruction.

Mayes, R.L., 1994, “Measurement of Lateral Launch Loads on Re-Entry Vehicles Using SWAT”

Conference: Uses the SWAT method to reconstruct forces acting on a structure, but uses the free decay time histories to calculate the weights.

Liu, Y., and Shepard, S., Jr., 2005, “Dynamic Force Identification Nased on Enhanced Least Squares and Total Least-Squares Schemes in the Frequency Domain”

Journal: Utilizes and compares the least-square method of indirect force estimation without regularization and with truncated SVD and regularization.

1. Chae, C.K., Bae, B.K., Kim, K.J., Park, J.H. and Choe, N.C., “A Feasibility Study in Indirect Identification of Transmission Forces through Rubber Bushing in Vehicle Suspension System by Using Vibration Signals Measured on Links,” 1999, Vehicle System Dynamics, Vol. 33, No. 5, pp. 327-349.

2. Decker, M. and Savaidis, G., “Measurement and Analysis of Wheel Loads for Design and Fatigue Evaluation of Vehicle Chassis Components,” 2002, Fatigue and Fracture of Engineering Materials and Structures, Vol. 25, Issue 12, 1103.

3. O’Connor, C., and Chan, T.H.T., “Dynamic Wheel Loads from Bridge Strains,” 1998, J. Struct. Div. ASCE, 114(8), pp. 1703-1723.

4. Chan, T.H.T., Law, S.S., Yung, T.H. and Yuan, X.R., “An Interpretive Method for Moving Force Identification,” 1999, Journal of Sound and Vibration, 219(3), pp. 503-524.

5. Zhu, X.Q. and Law, S.S., “Identification of Vehicle Axle Loads from Bridge Responses,” 2000, Journal of Sound and Vibration, 236(4), pp. 705-724

6. Wang, M.L. and Kreitinger, T.J., “Identification of Force from Response Data of a Nonlinear System,” 1994, Soil Dynamics and Earthquake Engineering, Vol. 13, pp. 267-280.

7. Giergil, J. and Uhl, T., “Identification of the Input Excitation Forces in Mechanical Structures,” 1989, The Archives of Transport, Vol. 1, No. 1.

8. Haas, D.J., Milano and Flitter, L., “Prediction of Helicopter Component Loads Using Neural Networks,” 1995, Journal of the American Helicopter Society, No. 1, pp. 72-82.

9. Giasante, N., Jones, R. and Calapodas, N. J., “Determination of In-Flight Helicopter Loads,” 1983, Journal of the American Helicopter Society, 27, pp. 58-64.

10. Li, J., “Application of Mutual Energy Theorem for Determining Unknown Force Sources,” 1988, Proc. of Internoise 88, Avignion.

11. Zion, L., “Predicting Fatigue Loads Using Regression Diagnostics,” 1994, Proc. of the American Helicopter Society 50 Annual Forum, Washington D.C.



12. Uhl, T. and Pieczara, J., “Identification of Operational Loading Forces for Mechanical Structures,” 2003, The Archives of Transport, Vol. 16, No. 2.

  1. Stevens, K.K., “Force Identification Problems-An Overview,” 1987, Proc. of SEM Spring Conference on Experimental Mechanics, pp. 838-844.

  2. Starkey, J.M., and G.L. Merrill, “On the Ill-Conditioned Nature of Indirect Force-Measurement Techniques,” 1989, Journal of Modal Analysis, pp. 103-108.

  3. Bartlett, F.D., Jr., and W.G. Flannelly, “Model Verification of Force Determination for Measuring Vibratory Loads,” 1979, J. American Helicopter Society, 24:10-18.

  4. Hundhausen, R.J., D.E. Adams, M. Derriso, P. Kukuchek, and R. Alloway, “Transient Loads Identification for a Standoff Metallic Thermal Protection System Panel,” 2005, Proc. of the IMAC-XXIII: A Conference & Exposition on Structural Dynamics, No. 394.

  5. Turco, E., “A Strategy to Identify Exciting Forces Acting on Structures,” 2005, International Journal for Numerical Methods in Engineering, 64:1483-1508.

  6. Kammer, D.C., “Input Force Reconstruction Using a Time Domain Technique,” 1996, American Institute of Aeronautics and Astronautics, Inc., pp. 21-30.

  7. Jacquelin, E., A. Bennani, and P. Hamelin, “Force Reconstruction: Analysis and Regularization of a Deconvolution Problem,” 2003, Journal of Sound and Vibration, 265: 81-107.

  8. Fabunmi, J.A., “Effects of Structural Modes on Vibratory Force Determination by the Pseudoinverse Technique,” 1986, American Institute of Aeronautics and Astronautics, Inc., 24(3):504-509.

  9. Carne, T.G., R.L. Mayes, and V.I. Bateman, “Force Reconstruction Using the Sum of Weighted Acceleration Technique--Max-Flat Procedure,” 1994, Proc. of 12th International Modal Analysis Conference, pp. 1054-1062.

  10. Mayes, R.L., “Measurement of Lateral Launch Loads on Re-entry Vehicles Using SWAT,” 1994, Proc. of 12th International Modal Analysis Conference, pp. 1063-1068.

  11. Liu, Y., and S. Shepard, Jr., “Dynamic Force Identification Based on Enhanced Least Squares and Total Least-Squares Schemes in the Frequency Domain,” 1995, Journal of Sound and Vibration, 282: 37-60.


Table B.12 – References on vibration-based damage identification methods.

Reference

Summary

Doebling et al., 1996, “Damage Identification and Health Monitoring of Structural and Mechanical Systems from Changes in Their Vibration Characteristics: A Literature Review”

Report: Comprehensive survey of vibrations-based techniques for damage detection, location and characterization.

Hoon et al., 2001, “A Review of Structural Health Monitoring Literature: 1996-2001”

Report: An update to the work by Doebling et al. (1996) that outlines feature extraction and damage quantification methods among other issues.

Afolabi, D., 1987, “An Anti-Resonance Technique for Detecting Structural Damage”

Conference: Showed how data around anti-resonances is much more sensitive to structural damage compared to the resonances.

Zhang et al., 1999, “Structural Health Monitoring Using Transmittance Functions”

Journal: Showed that transmissibility functions are reliable detection features to locate perturbations in experiments on a composite beam.

Johnson, T. J. and Adams, D. E., 2002, "Transmissibility as a Differential Indicator of Structural Damage"

Journal: Developed a transmissibility-based detection feature that was able to detect and locate damage.

Wang, W. and Zhang, A., 1987, “Sensitivity Analysis in Fault Vibration Diagnosis of Structures”

Conference: Determined that certain frequency ranges in FRFs, including those near anti-resonances, are sensitive to changes in structural parameters.

I. Trendafilova et al., 1998, “Damage Localization in Structures. A Pattern Recognition Perspective”

Conference: Presented a pattern recognition approach for damage localization in structures.

Sohn, H. and Farrar, C.F., 2001, “Damage Diagnosis Using Time Series Analysis of Vibration Signals”

Journal: Used standard deviation of residual errors from a combination of AR and ARX models as a damage-sensitive feature to locate damage.

Nair et al., 2003, “Application of Time Series Analysis in Structural Damage Evaluation”

Conference: Previous algorithm is modified to increase the effectiveness in identifying small damage patterns by using normalized relative accelerations.

Adams, D.E. and Farrar, C.R., 2002, “Classifying Linear And Non-Linear Structural Damage Using Frequency Domain ARX Models”

Journal: Used frequency domain autoregressive models to develop linear and nonlinear damage features in a three-story building frame.

Johnson et al., 2005, “Embedded Sensitivity Functions for Characterizing Structural Damage”

Journal: Presented the use of algebraic combinations of measured FRF data to estimate perturbations in mass, damping, or stiffness due to damage.

Adams, D.E., 2002, “Nonlinear Damage Models for Diagnosis and Prognosis in Structural Dynamic Systems”

Conference: Demonstrated that model reduction near bifurcations caused by structural damage is a useful way to identify damage features.

Farrar et al., 1999, “A Statistical Pattern Recognition Paradigm of Vibration-Based Structural Health Monitoring”

Conference: Discussed the process of vibration-based structural health monitoring as a statistical pattern recognition problem.

Corbin et al., 2000, “Locating Damage Regions Using Wavelet Approach”

Conference: Detected damage using wavelet decomposition of acceleration response data.

Moyo, P. and Brownjohn, J.M.W., 2002, “Detection of Anomalous Structural Behavior Using Wavelet Analysis”

Journal: Used wavelet analysis to detect anomalies using strain data from a bridge but does not distinguish damage from other sources of variability.

Sun, Z., and Chang, C.C., 2002, “Structural Damage Assessment Based on Wavelet Packet Transform”

Journal: Developed a damage assessment method using the wavelet packet transform to produce inputs to neural network models.

Hou et al., 2000, “Application Wavelet-Based Approach for Structural Damage Detection”

Journal: Showed that damage can be detected by decomposing response data using wavelets with the potential to locate damage as well.

Haroon, M., and Adams, D.E., 2005, “Active and Event-Driven Passive Mechanical Fault Identification in Ground Vehicle Suspension Systems”

Conference: Presented active and passive data interrogation methodologies for damage identification based on the frequency bandwidth of signals.

Haroon, M., and Adams, D.E., 2006, “Nonlinear Fault Identification Methods for Ground Vehicle Suspension Systems”

Conference: Discussed nonlinear damage identification methods which track nonlinear changes accompanying damage using response acceleration data.

Worden et al., 2003, “Experimental Validation of Structural Health Monitoring Methodology I: Novelty Detection on a Laboratory Structure”

Journal: Presented experimental verification of the novelty detection method for damage identification based on transmissibility functions.

Manson et al., 2003, “Experimental Validation of Structural Health Monitoring Methodology II: Novelty Detection on an Aircraft Wing”

Journal: Applied the previously discussed outlier analysis based novelty detection algorithm on a realistic structure, the wing of a Gnat aircraft.

Monaco, E., Calandra, G., and Lecce, L., 2000, “Experimental Activities on Damage Detection Using Magnetorestricitve Actuators and Statistical Analysis”

Conference: Used averages of differences between healthy and damaged structure FRFs as damage detection features.

Natke, H.G., and Cempel, C., 1997, “Model-Aided Diagnosis Based on Symptoms”

Conference: Used changes in natural frequencies and mode shapes in a finite element model of a cable-stayed steel bridge to detect damage.

Garcia et al., 1998, “Comparison of the Damage Detection Results Utilizing an ARMA Model and a FRF Model to Extract Modal Parameters”

Conference: Time domain ARMA model and FRF modal extraction techniques are compared, and ARMA model out performs modal parameters.

Garcia, G., and Osegueda, R., 1999, “Damage Detection Using ARMA Model Coefficients”

Conference: Parameters of time domain ARMA model are used for damage detection; location was possible with ambiguity for multiple damage sites.

Sohn, H. and Farrar, C.R., 2000, “Statistical Process Control and Projection Techniques for Structural Health Monitoring”

Conference: Combined statistical process control with projection techniques, such as principal component analysis, for damage detection.

Bodeux, J.B., and Golinval, J.C., 2000, “ARMAV Model Technique for System Identification and Damage Detection”

Conference: Demonstrated the use of time-domain Auto-Regressive Moving-Average Vector (ARMAV) models for detecting damage.

Heyns, P.S., 1997, “Structural Damage Assessment Using Response-Only Measurements”

Conference: Used a Multivariate Auto-Regressive Vector (ARV) model based approach to detect and locate damage in a cantilever beam.

Tsyfansky, S.L. and Beresnevich, V.I., 1997, “Vibrodiagnosis of Fatigue Cracks in Geometrically Nonlinear Beams”

Conference: Attempted to detect and quantify fatigue cracks in a beam by analyzing the nonlinear harmonics in the Fourier spectrum of the response.

Masri et al., 2000, “Application of Neural Networks fort Detection of Changes in Nonlinear Systems”

Journal: Presented a neural network technique for health monitoring using vibration measurements; prediction error was used for detecting damage.

Feng, M., and Bahng, E., 1999, “Damage Assessment of Bridges with Jacketed RC Columns Using Vibration Test”

Conference: Proposed a jacketed column monitoring method that combines vibration testing, neural network, and finite element techniques.

Worden, K. and Fieller, N.R.J., 1999, “Damage Detection Using Outlier Analysis”

Journal: Studied outlier analysis for damage detection with a Mahalanobis distance based on measured transmissibility functions as damage feature.

Salawu, O.S., 1997, “Detection of Structural Damage through Changes in Frequency: A Review”

Journal: Reviewed methods for detecting damage using natural frequencies and discussed relationships between frequency changes and structural damage.

Farrar, C.R., 1997, “Variability of Modal Parameters on the Alamosa Canyon Bridge”

Doebling et al. 1997, “Effects of Measurements Statistics on the Detection of Damage in the Alamosa Canyon Bridge”



Conference: Showed that the sensitivity of frequency shifts to damage is low but these shifts exhibit less statistical variation from random error.

Cawley, P., and Adams, R.D., 1979, “Location of Defects in Structures from Measurements of Natural Frequencies”

Journal: Detected damage in composite materials using ratios between frequency shifts for two different modes.

Pandey et al., 1991, “Damage Detection from Changes in Curvature Mode Shapes”

Journal: Showed that absolute changes in mode shape curvature can be a good indicators of damage.

Pandey, A.K. and Biswas, M., 1994, “ Damage Detection in Structures Using Changes in Flexibility”

Pandey, A.K. and Biswas, M., 1995, “Damage Diagnosis of Truss Structures by Estimation of Flexibility Change”



Journal: Presented a damage detection and location method based on changes in the measured flexibility matrix using lowest frequency vibration modes.

Lim, T.W., 1991, “Structural Damage Detection Using Modal Test Data”

Journal: Used the unity check methods for damage detection by defining a least-squares problem for the elemental stiffness changes in a truss.

Banks, H. T., Inman, D. J., Leo, D. J., Want, Y., 1996, “An Experimentally Validated Damage Detection Theory in Smart Structures”

Journal: Developed a damage detection theory based on the derivative of frequency with respect to either stiffness or mass.

Doebling, S. W., 1996, “Minimum-Rank Optimal Update of Elemental Stiffness Parameters for Structural Damage Identification”

Journal: Developed an optimal minimum-rank update of stiffness parameters for damage identification.

Escobar, J. A., Sosa, J. J., Gomez, R., 2005, “Structural Damage Detection using the Transformation Matrix”

Journal: Used transformation matrix in two- and three-dimensional analytical building models to detect damage.

Fritzen, C. P., Jennewein, D., Kiefer, T., 1998, “Damage Detection Based on Model Updating Methods”

Journal: Applied a sensitivity approach that used both time and frequency to localize damage in a finite element beam model.

Hajela, P. and Soeiro, F. J., 1989, “Structural Damage Detection Based on Static and Modal Analysis”

Journal: Eigenmodes and static displacements were used to detect changes in stiffness.

Hwang, H.Y., Kim C., 2004, “Damage detection using a few frequency response measurements”

Journal: Modeled damage using changes in the component stiffness matrix and treated the damage detection problem as a minimization problem.

Lew, J. S., 1995, “Using Transfer Function Parameter Changes for Damage Detection of Structures”

Journal: Found that changes in environmental factors contribute less significantly to the structural natural frequencies than actual damage.

Kaouk, M., Zimmerman, D. C., 1994, “Structural Damage Assessment Using a Generalized Minimum Rank Perturbation Theory”

Journal: Addressed unsymmetric impedance matrices with singular value decomposition to acquire a damage vector.

Samuel, P. D., Pines, D. J., 2004, “A Review of Vibration-based Techniques for Helicopter Transmission Diagnostics”

Journal: Points out progress in the area of vibration-based fault detection.

Sheinman, I., 1996, “Damage Detection and Updating of Stiffness and Mass Matrices using Mode Data”

Journal: Damage was detected using minimal static and dynamic measurements through a closed form algorithm.

Tsuei, Y. G., Yee, E. K. L., 1989, “A Method for Modifying Dynamic Properties of Undamped Mechanical Systems”

Journal: Modified mass and stiffness matrices by adding small changes in mass and stiffness to the forcing function of the unmodified structure.

Zimmerman, D. C., Kaouk, M., 2005, “Model Correlation and System Health Monitoring using Frequency Domain Measurements”

Journal: Addressed unsymmetric impedance matrices with singular value decomposition to acquire a damage vector.




  1. Doebling, S.W., Farrar, C.R., Prime, M.B. and Shevitz. D.W., “Damage Identification and Health Monitoring of Structural and Mechanical Systems from Changes in Their Vibration Characteristics: A Literature Review,” 1996, Los Alamos National Laboratory report, LA-13070-MS.

  2. Sohan, H., Farrar, C.R., Hemez, F.M., Shunk, D.D., Stinemates, D.W. and Nadler, B.R., 20031, “A review of structural health monitoring literature: 1996-2001,” Los Alamos National Laboratory report, LA-13976-MS.

  3. Afolabi, D., “An Anti-Resonance Technique for Detecting Structural Damage,” 1987, Proc. of the 5th International Modal Analysis Conference, pp. 491-495.

  4. Zhang, H., Schulz, M. J., Naser, A., Ferguson, F., and Pai, P.F., “Structural Health Monitoring Using Transmittance Functions,” 1999, Mechanical Systems and Signal Processing, 13(5), pp. 765-787.

  5. Johnson, T. J. and Adams, D. E., “Transmissibility as a Differential Indicator of Structural Damage,” 2002, ASME Journal of Vibration and Acoustics, 124(4), pp. 634-641.

  6. Wang, W. and Zhang, A., “Sensitivity Analysis in Fault Vibration Diagnosis of Structures,” 1987, Proc. of the 5th International Modal Analysis Conference, pp. 496-501.

  7. Trendafilova, I., Heylen, W., Sas, P., “Damage Localization in Structures. A Pattern Recognition Perspective,” 1998, ISMA 23, pp. 99-106.

  8. Sohn, H. and Farrar, C.F., “Damage Diagnosis Using Time Series Analysis of Vibration Signals,” 2001, Smart Materials and Structures, Vol. 10, pp. 446-451.

  9. Nair, K.K., Kiremidjian, A.S., Lei, Y., Lynch, J.P., and Law, K.H., “Application of Time Series Analysis in Structural Damage Evaluation,” 2003, Proc. of the International Conference on Structural Health Monitoring, Tokyo, Japan.

  10. Adams, D.E. and Farrar, C.R., “Classifying Linear and Non-linear Structural Damage Using Frequency Domain ARX Models,” 2002, Structural Health Monitoring, 1(2), pp.185-201.

  11. Johnson, T.J., Yang, C., Adams, D.E., and Ciray, S., “Embedded Sensitivity Functions for Characterizing Structural Damage,” 2005, Smart Materials and Structures, Vol. 14, pp. 155-169.

  12. Adams, D.E., “Nonlinear Damage Models for Diagnosis and Prognosis in Structural Dynamic Systems,” 2002, SPIE, Vol. 4733.

  13. Farrar, C.R., Duffey, T.A., Doebling, S.W., and Nix, D.A., “A Statistical Pattern Recognition Paradigm of Vibration-Based Structural Health Monitoring,” 1999, 2nd International Workshop on Structural Health Monitoring, Stanford, CA, pp. 764-773.

  14. Corbin, M., Hera, A., and Hou, Z., “Locating Damage Regions Using Wavelet Approach,” 2000, Proc. of the 14th Engineering Mechanics Conference (EM2000), Austin, Texas.

  15. Moyo, P. and Brownjohn, J.M.W., “Detection of Anomalous Structural Behavior Using Wavelet Analysis,” 2002, Mechanical Systems and Signal Processing, Vol. 16(2-3), pp. 429-445.

  16. Sun, Z., and Chang, C.C., “Structural Damage Assessment Based on Wavelet Packet Transform,” 2002, Journal of Structural Engineering, Vol. 128(10), pp. 1354-1361.

  17. Hou et al., “Application Wavelet-Based Approach for Structural Damage Detection,” 2000, Journal of Engineering Mechanics, Vol. 126(7), pp. 677-683

  18. Haroon, M., and Adams, D.E., “Active and Event-Driven Passive Mechanical Fault Identification in Ground Vehicle Suspension Systems,” 2005, Proc. of IMECE: ASME International Mechanical Engineering Congress and Exposition, Orlando, FL, Paper #: 80582.

  19. Haroon, M., and Adams, D.E., “Nonlinear Fault Identification Methods for Ground Vehicle Suspension Systems,” 2006, IMAC-XXIV, St. Louis, MO, Paper #: 44.

  20. Worden, K., Manson, G., and Allman, D., “Experimental Validation of Structural Health Monitoring Methodology I: Novelty Detection on a Laboratory Structure,” 2003, Journal of Sound and Vibration, Vol. 259, pp. 323-343.

  21. Manson, G., Worden, K., and Allman, D., “Experimental Validation of Structural Health Monitoring Methodology II: Novelty Detection on an Aircraft Wing,” 2003, Journal of Sound and Vibration, Vol. 259, pp. 343-363.

  22. Monaco, E., Calandra, G., and Lecce, L., “Experimental Activities on Damage Detection Using Magnetorestricitve Actuators and Statistical Analysis,” 2000, Smart Structures and Materials 2000: Smart Structures and Integrated Systems, Proc. of SPIE, Vol. 3985, pp. 186-196.

  23. Natke, H.G., and Cempel, C., “Model-Aided Diagnosis Based on Symptoms,” 1997, Structural Damage Assessment Using Advanced Signal Processing Procedures, Proc. of DAMAS ’97, Univ. of Sheffield, UK, pp. 363-375.

  24. Garcia, G., Osegueda, R. and Meza, D., “Comparison of the Damage Detection Results Utilizing an ARMA Model and a FRF Model to Extract Modal Parameters,” 1998, Smart Systems for Bridges, Structures, and Highways, Proc. of SPIE, Vol. 3325, pp. 244-252.

  25. Garcia, G., and Osegueda, R., “Damage Detection Using ARMA Model Coefficients,” 1999, Smart Systems for Bridges, Structures, and Highways, Proc. of SPIE, Vol. 3671, pp. 289-296.

  26. Sohn, H. and Farrar, C.R., “Statistical Process Control and Projection Techniques for Structural Health Monitoring,” 2000, European COST F3 Conference on System Identification and Structural Health Monitoring, Madrid, Spain, pp. 105-114.

  27. Bodeux, J.B., and Golinval, J.C., “ARMAV Model Technique for System Identification and Damage Detection,” 2000, European COST F3 Conference on System Identification and Structural Health Monitoring, Madrid, Spain, pp. 303-312.

  28. Heyns, P.S., “Structural Damage Assessment Using Response-Only Measurements,” 1997, Structural Damage Assessment Using Advanced Signal Processing Procedures, Proceeding of DAMAS ’97, Univ. of Sheffield, UK, pp. 213-223.

  29. Tsyfansky, S.L. and Beresnevich, V.I., “Vibrodiagnosis of Fatigue Cracks in Geometrically Nonlinear Beams,” 1997, Structural Damage Assessment Using Advanced Signal Processing Procedures, Proceeding of DAMAS ’97, Univ. of Sheffield, UK, pp. 299-311.

  30. Masri, S.F., Smyth, A.W., Chassiakos, A.G., Caughey, T.K., and Hunter, N.F., “Application of Neural Networks fort Detection of Changes in Nonlinear Systems,” 2000, Journal of Engineering Mechanics, July, pp. 666-676.

  31. Feng, M., and Bahng, E., “Damage Assessment of Bridges with Jacketed RC Columns Using Vibration Test,” 1999, Smart Structures and Materials 1999: Smart Systems for Bridges, Structures, and Highways, Proc. of SPIE, Vol. 3671, pp. 316-327.

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