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Appendix B
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
B.2 Sensors
In
Table B.3-Table B.10, different types of displacement, velocity, acceleration, strain, force,
temperature, and pressure sensors are summarized.
B.3 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.
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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
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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
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Table B.3 – Displacement sensors.
Sensor Type:
Inductive
Capacitive
Gyroscope
Magnetic
Optical
Ultrasonic
Acoustic Emission
DRAFT
Advantages
Disadvantages
Applications
Example
1. Low susceptibility to noise and
1. Accurate for small distance (1mm1. Crack detection in turbine
interference
150mm)
blades
2. Accurate at high temperatures
2. Surface conditions affect high
2. Corrosion thinning
while being unsusceptible to
resolution measurements
measurements on aircraft
enviromental conditions
skins
1. Can be used on conductive and 1. Sensitive to environmental
1. Measure aircraft engine
non-conductive materials
parameters
door cowling gaps
2. Wide bandwidth and high
2. Susceptible to electrostatic
2. Monitor aircraft cargo door
resolutions
charge due to friction
alignment.
1. Capable of operating under hostile1. Mechanical gyros accumulate
1. Measuring angular
environments
drift between actual and sensed
displacement of aircraft wings
values over time
due to turbulence.
2. Have a high signal to noise ratio 2. Provide only relative information
and low power consumption
1. Are more stable in noisy
1. Are susceptible to external
environments
magnetic interference.
2. Capable of achieveing low
2. Can only be used for
temperature sensitivity.
ferromagnetic materials.
1. Absence of loading effects on the 1.Not suitable to be bent at steep
structure.
angles due to refraction of light
2. Insensitivity to stray magnetic fields2. Fibers are delicate and can be
or electrostatic interference
easily damaged.
2. Satellite position monitoring
and control
1. Monitor crackshaft for
ignition timing and misfire
2. Monitoring weld health in
welded steel armor plates
1. Monitoring hull deflection on
a composite patrol boat
2. Measure displacement of
composite bridge decks due to
automotive loading.
1. Resistant to external disturbances 1. Sensors have a "dead" region 1. Study of wear,
such as vibration, ambient noise anddirectly below them where damage chipping/breakage and
EM radiation.
cannot be detected.
temperature in tooling parts
2. Capable of detecting small defects2. Time consuming and requires
at large distances
higher level of user skill
1. Is less sensitive to material surface1. Susceptible ot extraneous noise
roughness or geometry
2. High sensitivities allow for crack 2. Sensors must be mounted to
formation detection.
surface, resulting in possible mass
loading issues
2. Examining bolts or rivets in
aircraft wings
1. Monitor seal and blade-tip
rubbing in turbo machinery
2. Damage assessment in a
steel-concrete composite
bridge deck
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Table B.4 – Velocity sensors.
Sensor Type:
Magnetic Induction
Advantages
1. High sensitivity and excellent noise
immunity
2. Lower output, for use in high speed
applications
Disadvantages
1. Susceptible to electromagnetic
field interference
2. Must be mounted perpendicular
to plane of motion
Applications
Example
1. Measuring gear speed in an
automotive gearbox
2. Measure rotational speeds
in gas turbine engines
1. High accuracy and high reliability
Optical
Piezoelectric
DRAFT
1. Difficult to measure parts in hard 1. Monitoring vibrations of
to reach areas
automotive tires
2. Not affected by surface roughness 2. Requires powered light source
2. Monitor molten plastic flow
or color
in injection molding process
1. Larger frequency range then
magnetic sensors
2. Reduces signal noise for high
frequency measurements.
1. Response is nonlinear for low
frequencies (<10Hz)
2. Requires sensor to be mounted
to structure.
1. Measure vibrations in
cavitating pumps.
2. Monitor seals in paper
handling machines.
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Table B.5 – Acceleration sensors.
Sensor Type:
Capacitive
MEMS
Piezoelectric
Piezoresistive
DRAFT
Advantages
Disadvantages
Applications
1. Higher sensitivities than
piezoresistive accelerometers.
2. Measures static acceleration
1. Must compensate for drift and
interfernce affects.
2. Low resolution and fragile
1. Measure aircraft wing flutter
response
2. Measure hard disk drive
acceleration due to writing
process
1. Small, lightweight, high g
acceleration.
1. Performance/Specifications can
degrad over time
1. Used for automotive airbag
devlopment measurements
2. Lower cost then other
accelerometers
2. Expensive to repair due to their
small size.
2. Monitor laptop computer
vibration and stop harddrive
processes to prevent damage
1. Wide dynamic range, low output
noise
1. Low bandwidth, not suited for low 1. Measuring vibration
frequency testing.
response in an exhaust system
2. Can produce high output voltage
2. Requires sensor to be mounted
to structure resulting in possible
mass loading affects
2. Measuring acceleration
response of TPS panel impact.
1. Not adversely affected by
electromagnetic fields
2. Measures static acceleration
1. Limited resolution due to resistive
noise
2. Primarily for low to mid frequency
applications
1. Measure accelerations of
ejection seats
2. Measure crash test dummy
acceleration due to collisions
Example
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Table B.6 – Strain sensors.
Sensor Type:
Piezoresistive
Advantages
Disadvantages
1. Capable of recognizing static
forces
2. Simplicity of mounting to the
surface
1. Requires sensor to be mounted
to structure
2. Susceptible to external sources of
noise and temperature
Applications
1. Not susceptible to electromagnetic 1. Requires fiber optic cable to be
interference
run to each sensor
1. Strain monitoring of civil
structures, for instance
bridges, dams, buildings,
pipelines .
2. Multiplexing capability
2. Monitoring ship hul strains
Optical
2. Requires a power source
Example
1. Measure strains in gas
turbine fan blades
2. Measure helicopter blade
deflections
Table B.7 – Force sensors.
Sensor Type:
Piezoresistive
Optical
DRAFT
Advantages
1. High stiffness allows direct
insertion in machine structures
2. High natural frequencies, ideal for
quick transient forces
1.Multiplexing capability
2. Ideal for high temperature
applications
Disadvantages
Applications
Example
1. More expensive then other types 1. Recording impact forces in
military applications
2. The output can be nonlinear
2. Measuring wave forces on
off-shore oil platforms
1. Requires fiber optic cable to be
1. Monitoring traffic loads over
run to each sensor
the span of a bridge
2. Requires a power source
2. Measure clamping force of a
car window closing
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Table B.8 – Temperature sensors.
Sensor Type:
Acoustic
Optical
Thermoresistive
Thermoelectric
Advantages
Disadvantages
1. Capable of operating in cryogenic 1. Susceptible to external sources of
temperature range
noise
2. Immune to high levels of radiation 2. Sensors must be mounted to
surface
Applications
Example
1. Measuring temperature inside
catalytic converters
2. Temperature measurement
for feedback control of engine
combustion
1. Negligible electromagnetic
interference affects.
2. Small and flexible for easy
installation
1. fiber optic cables are delicate and 1. Measuring temperature of
limit maximum temperature
electric generators
2. Slow data processing
2. Temperature monitoring in
semi-conductor manufacturing
1. Typically cheaper than other
sensors
1. Resistance vs. temperature is
nonlinear causing limited
temperature range
1. Measure automotive engine
oil and coolant temperatures
2. Easy implementation due to small 2. Limited operating temperature
size
range
2. Measure inside air
temperature in HVAC systems
1. Offer higher temperature range
then thermoresistive sensors
1. Have upper temperature limit of
3100F
1. Engine and turbine exhaust
gas monitoring
2. Are cheapest of all temperature
sensors
2. Measured temperatures drift over 2. Heat treating and metals
time
processing temperatures
Table B.9 – Pressure sensors.
Sensor Type:
Piezoresistive
DRAFT
Advantages
Disadvantages
Applications
1. Measure both static and dynamic
pressures
1. Increase in pressure might lead
to transducer becoming nonlinear
2. Reliable under varying
environmental conditions
2. Can produce significant electrical 2. Measure jet engine
noise.
pressure at inlet and outlet of
each component
Example
1. Measure engine combustion
chamber pressures
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Table B.10 – Piezoelectric actuators.
Configuration: Sensing Direction:
Transverse
1. Ideal for static and low
frequency applications.
2. Capable of applying tension
and compression loads
Shear
1. Extremely reliable (>109
cycles).
2. High resonant frequencies.
Stack
Tube
Ring
Disk
Bimorph (PVDF)
DRAFT
Advantages:
Transverse
Transverse
Transverse
Transverse/Shear
1. Capable of measuring
displacements along all three
axes.
2. Sub-nanometer resolution
1. Available with clear
aperatures for transmitted-light
applications.
2. High resolution for
static/dynamic applications.
1. Provide a relatively large
travel range for their size.
2. Fast response w/ subnanometer resolution
1. Low operating voltage.
2. Excellent resistance to
humidity.
Disadvantages:
1. Low electro-mechanical
coupling
2. Requires strong bonds to
ensure high fidelity.
3. Stability problems for large
displacement
1. Needs to be pre-loaded to
avoid un-poling resulting in
lowered operational
frequencies.
Applications:
1. Fine tuning of laser
equipment.
2. Alignment of fiber optics.
3. Control injection valves in
the automotive industry
1. Atomic force microscopy.
2. Active vibration
cancellation.
1. Small Displacement
1. Hard drive read/write head
2. Relative to stack actuators, testing.
small force
2. Needle valve actuation.
1. More delicate than other
configurations due to the
center bore
2. Low force
1. Image positioning.
2. Micropositioning
1. Low force
1. Knife edge control in
extrusion tools.
2. Tuning of circular boring,
drilling processes.
1. Low frequency operation.
2. Low resolution (unsuitable
for precision).
3.
Low force and slow response
1. Position control of
pneumatic valves.
2. Measuring accelerations of
flexible structures.
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Table B.11 – References on methods for loads identification.
Reference
Stevens, K.K., 1987, “Force Identification Problems-An Overview”
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”
Decker, M. and Savaidis, G., 2002, “Measurement and Analysis of Wheel
Loads for Design and Fatigue Evaluation of Vehicle Chassis Components”
O’Connor, C., and Chan, T.H.T., 1988, “Dynamic Wheel loads From Bridge
Strains”
Chan, T.H.T., Law, S.S., Yung, T.H. and Yuan, X.R., 1999, “An Interpretive
Method for Moving Force Identification”
Zhu, X.Q. and Law, S.S., 2000, “Identification of Vehicle Axle Loads from
Bridge Responses”
Wang, M.L. and Kreitinger, T.J., 1994, “Identification of Force from Response
Data of a Nonlinear System”
Giergil, J. and Uhl, T., 1989, “Identification of the Input excitation forces in
mechanical structures”
Summary
Conference: Overview of indirect force estimation for linear systems.
Journal: Relates the transmission force to the deformation of rubber bushings
through an appropriate model.
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.
Conference: Identified spectrum of loads based on vibration velocity
response measurements.
Li, J., 1988, “Application of Mutual Energy Theorem for Determining
Unknown Force Sources”
Zion, L., 1994, “Predicting Fatigue Loads Using Regression Diagnostics”
Journal: Discussed the interactions of wheel forces and moments, forces
acting in a suspension, and the stress response of an axle casing.
Journal: Modeled the bridge deck as lumped masses interconnected by massless elastic beams and estimated loading of bridge due to wheels.
Journal: Modeled the bridge deck using Bernoulli-Euler beams and estimated
loading of bridge due to wheels.
Journal: Modeled the bridge deck as orthotropic plates and estimated loading
of bridge due to wheels.
Journal: Presented the sum of weighted acceleration technique (SWAT) to
estimate the input force.
Journal: Presented an iterative formula for calculation of excitation forces in
mechanical structures based on properties of the Toeplitz matrix.
Uhl, T. and Pieczara, J., 2003, “Identification of Operational Loading Forces
for Mechanical Structures”
Conference: Presented an approach based on a regression model relating
loads and flight data in a helicopter.
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”
Bartlett, F.D., Jr., and W.G. Flannelly, 1979, “Model Verification of Force
Determination for Measuring Vibratory Loads”
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.
Journal: Found that the pseudo-inverse method of force estimation worked
well for identifying vibrations forces on the rotary hub of a helicopter model
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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”
Turco, E., 2005, “A Strategy to Identify Exciting Forces Acting on Structures”
Kammer, D.C., 1996, “Input Force Reconstruction Using a Time Domain
Technique”
Jacquelin, E., Bennani, A., and Hamelin, P, 2003, “Force Reconstruction:
Analysis and Regularization of a Deconvolution Problem”
Fabunmi, J.A., 1986, “Effects of Structural Modes on Vibratory Force
Determination by the Pseudoinverse Technique”
Carne, T.G., Mayes, R.L., and Bateman, V.I., 1994, “Force Reconstruction
Using the Sum of Weighted Acceleration Technique—Max-Flat Procedure”
Mayes, R.L., 1994, “Measurement of Lateral Launch Loads on Re-Entry
Vehicles Using SWAT”
Liu, Y., and Shepard, S., Jr., 2005, “Dynamic Force Identification Nased on
Enhanced Least Squares and Total Least-Squares Schemes in the Frequency
Domain”
© D. Adams 2006
Conference: Used two methods for identifying transient loads on standoff
metallic panels: 1) rigid body approach, and 2) inverse FRF approach.
Journal: Explores the use of the Tikhonov regularization technique to reduce
ill-conditioning effects of frequency domain equations for pin-jointed trusses.
Journal: Convolves the measured response and an inverse system of Markov
parameters to estimate input forces on a structure in the time domain.
Journal: Applies Tikhonov and trunctation regularization techniques to the
indirect force estimation problem and chooses the regularization parameters.
Journal: Studied the implication of using the least-squares method of force
identification without considering the modes and mode shapes.
Conference: Used FRF data to determine appropriate scalar weights to use in
the Sum of Weighted Acceleration Technique for force reconstruction.
Conference: Uses the SWAT method to reconstruct forces acting on a
structure, but uses the free decay time histories to calculate the weights.
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.
13. Stevens, K.K., “Force Identification Problems-An Overview,” 1987, Proc. of SEM Spring Conference on Experimental Mechanics, pp. 838-844.
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14. 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.
15. 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.
16. 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.
17. Turco, E., “A Strategy to Identify Exciting Forces Acting on Structures,” 2005, International Journal for Numerical Methods in Engineering, 64:1483-1508.
18. Kammer, D.C., “Input Force Reconstruction Using a Time Domain Technique,” 1996, American Institute of Aeronautics and Astronautics, Inc., pp. 21-30.
19. 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.
20. 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.
21. 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.
22. 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.
23. 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
Doebling et al., 1996, “Damage Identification and Health Monitoring of
Structural and Mechanical Systems from Changes in Their Vibration
Characteristics: A Literature Review”
Hoon et al., 2001, “A Review of Structural Health Monitoring Literature:
1996-2001”
Afolabi, D., 1987, “An Anti-Resonance Technique for Detecting Structural
Damage”
Zhang et al., 1999, “Structural Health Monitoring Using Transmittance
Functions”
Johnson, T. J. and Adams, D. E., 2002, "Transmissibility as a Differential
Indicator of Structural Damage"
Wang, W. and Zhang, A., 1987, “Sensitivity Analysis in Fault Vibration
Diagnosis of Structures”
I. Trendafilova et al., 1998, “Damage Localization in Structures. A Pattern
Recognition Perspective”
Sohn, H. and Farrar, C.F., 2001, “Damage Diagnosis Using Time Series
DRAFT
Summary
Report: Comprehensive survey of vibrations-based techniques for damage
detection, location and characterization.
Report: An update to the work by Doebling et al. (1996) that outlines feature
extraction and damage quantification methods among other issues.
Conference: Showed how data around anti-resonances is much more sensitive
to structural damage compared to the resonances.
Journal: Showed that transmissibility functions are reliable detection features
to locate perturbations in experiments on a composite beam.
Journal: Developed a transmissibility-based detection feature that was able to
detect and locate damage.
Conference: Determined that certain frequency ranges in FRFs, including those
near anti-resonances, are sensitive to changes in structural parameters.
Conference: Presented a pattern recognition approach for damage localization
in structures.
Journal: Used standard deviation of residual errors from a combination of AR
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Analysis of Vibration Signals”
Nair et al., 2003, “Application of Time Series Analysis in Structural Damage
Evaluation”
Adams, D.E. and Farrar, C.R., 2002, “Classifying Linear And Non-Linear
Structural Damage Using Frequency Domain ARX Models”
Johnson et al., 2005, “Embedded Sensitivity Functions for Characterizing
Structural Damage”
Adams, D.E., 2002, “Nonlinear Damage Models for Diagnosis and Prognosis
in Structural Dynamic Systems”
Farrar et al., 1999, “A Statistical Pattern Recognition Paradigm of VibrationBased Structural Health Monitoring”
Corbin et al., 2000, “Locating Damage Regions Using Wavelet Approach”
Moyo, P. and Brownjohn, J.M.W., 2002, “Detection of Anomalous Structural
Behavior Using Wavelet Analysis”
Sun, Z., and Chang, C.C., 2002, “Structural Damage Assessment Based on
Wavelet Packet Transform”
Hou et al., 2000, “Application Wavelet-Based Approach for Structural Damage
Detection”
Haroon, M., and Adams, D.E., 2005, “Active and Event-Driven Passive
Mechanical Fault Identification in Ground Vehicle Suspension Systems”
Haroon, M., and Adams, D.E., 2006, “Nonlinear Fault Identification Methods
for Ground Vehicle Suspension Systems”
Worden et al., 2003, “Experimental Validation of Structural Health Monitoring
Methodology I: Novelty Detection on a Laboratory Structure”
Manson et al., 2003, “Experimental Validation of Structural Health Monitoring
Methodology II: Novelty Detection on an Aircraft Wing”
Monaco, E., Calandra, G., and Lecce, L., 2000, “Experimental Activities on
Damage Detection Using Magnetorestricitve Actuators and Statistical
Analysis”
Natke, H.G., and Cempel, C., 1997, “Model-Aided Diagnosis Based on
Symptoms”
Garcia et al., 1998, “Comparison of the Damage Detection Results Utilizing an
ARMA Model and a FRF Model to Extract Modal Parameters”
Garcia, G., and Osegueda, R., 1999, “Damage Detection Using ARMA Model
Coefficients”
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and ARX models as a damage-sensitive feature to locate damage.
Conference: Previous algorithm is modified to increase the effectiveness in
identifying small damage patterns by using normalized relative accelerations.
Journal: Used frequency domain autoregressive models to develop linear and
nonlinear damage features in a three-story building frame.
Journal: Presented the use of algebraic combinations of measured FRF data to
estimate perturbations in mass, damping, or stiffness due to damage.
Conference: Demonstrated that model reduction near bifurcations caused by
structural damage is a useful way to identify damage features.
Conference: Discussed the process of vibration-based structural health
monitoring as a statistical pattern recognition problem.
Conference: Detected damage using wavelet decomposition of acceleration
response data.
Journal: Used wavelet analysis to detect anomalies using strain data from a
bridge but does not distinguish damage from other sources of variability.
Journal: Developed a damage assessment method using the wavelet packet
transform to produce inputs to neural network models.
Journal: Showed that damage can be detected by decomposing response data
using wavelets with the potential to locate damage as well.
Conference: Presented active and passive data interrogation methodologies for
damage identification based on the frequency bandwidth of signals.
Conference: Discussed nonlinear damage identification methods which track
nonlinear changes accompanying damage using response acceleration data.
Journal: Presented experimental verification of the novelty detection method
for damage identification based on transmissibility functions.
Journal: Applied the previously discussed outlier analysis based novelty
detection algorithm on a realistic structure, the wing of a Gnat aircraft.
Conference: Used averages of differences between healthy and damaged
structure FRFs as damage detection features.
Conference: Used changes in natural frequencies and mode shapes in a finite
element model of a cable-stayed steel bridge to detect damage.
Conference: Time domain ARMA model and FRF modal extraction techniques
are compared, and ARMA model out performs modal parameters.
Conference: Parameters of time domain ARMA model are used for damage
detection; location was possible with ambiguity for multiple damage sites.
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Sohn, H. and Farrar, C.R., 2000, “Statistical Process Control and Projection
Techniques for Structural Health Monitoring”
Bodeux, J.B., and Golinval, J.C., 2000, “ARMAV Model Technique for
System Identification and Damage Detection”
Heyns, P.S., 1997, “Structural Damage Assessment Using Response-Only
Measurements”
Tsyfansky, S.L. and Beresnevich, V.I., 1997, “Vibrodiagnosis of Fatigue
Cracks in Geometrically Nonlinear Beams”
Masri et al., 2000, “Application of Neural Networks fort Detection of Changes
in Nonlinear Systems”
Feng, M., and Bahng, E., 1999, “Damage Assessment of Bridges with Jacketed
RC Columns Using Vibration Test”
Worden, K. and Fieller, N.R.J., 1999, “Damage Detection Using Outlier
Analysis”
Salawu, O.S., 1997, “Detection of Structural Damage through Changes in
Frequency: A Review”
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”
Cawley, P., and Adams, R.D., 1979, “Location of Defects in Structures from
Measurements of Natural Frequencies”
Pandey et al., 1991, “Damage Detection from Changes in Curvature Mode
Shapes”
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”
Lim, T.W., 1991, “Structural Damage Detection Using Modal Test Data”
Banks, H. T., Inman, D. J., Leo, D. J., Want, Y., 1996, “An Experimentally
Validated Damage Detection Theory in Smart Structures”
Doebling, S. W., 1996, “Minimum-Rank Optimal Update of Elemental
Stiffness Parameters for Structural Damage Identification”
Escobar, J. A., Sosa, J. J., Gomez, R., 2005, “Structural Damage Detection
using the Transformation Matrix”
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© D. Adams 2006
Conference: Combined statistical process control with projection techniques,
such as principal component analysis, for damage detection.
Conference: Demonstrated the use of time-domain Auto-Regressive MovingAverage Vector (ARMAV) models for detecting damage.
Conference: Used a Multivariate Auto-Regressive Vector (ARV) model based
approach to detect and locate damage in a cantilever beam.
Conference: Attempted to detect and quantify fatigue cracks in a beam by
analyzing the nonlinear harmonics in the Fourier spectrum of the response.
Journal: Presented a neural network technique for health monitoring using
vibration measurements; prediction error was used for detecting damage.
Conference: Proposed a jacketed column monitoring method that combines
vibration testing, neural network, and finite element techniques.
Journal: Studied outlier analysis for damage detection with a Mahalanobis
distance based on measured transmissibility functions as damage feature.
Journal: Reviewed methods for detecting damage using natural frequencies
and discussed relationships between frequency changes and structural damage.
Conference: Showed that the sensitivity of frequency shifts to damage is low
but these shifts exhibit less statistical variation from random error.
Journal: Detected damage in composite materials using ratios between
frequency shifts for two different modes.
Journal: Showed that absolute changes in mode shape curvature can be a good
indicators of damage.
Journal: Presented a damage detection and location method based on changes
in the measured flexibility matrix using lowest frequency vibration modes.
Journal: Used the unity check methods for damage detection by defining a
least-squares problem for the elemental stiffness changes in a truss.
Journal: Developed a damage detection theory based on the derivative of
frequency with respect to either stiffness or mass.
Journal: Developed an optimal minimum-rank update of stiffness parameters
for damage identification.
Journal: Used transformation matrix in two- and three-dimensional analytical
building models to detect damage.
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Fritzen, C. P., Jennewein, D., Kiefer, T., 1998, “Damage Detection Based on
Model Updating Methods”
Hajela, P. and Soeiro, F. J., 1989, “Structural Damage Detection Based on
Static and Modal Analysis”
Hwang, H.Y., Kim C., 2004, “Damage detection using a few frequency
response measurements”
Lew, J. S., 1995, “Using Transfer Function Parameter Changes for Damage
Detection of Structures”
Kaouk, M., Zimmerman, D. C., 1994, “Structural Damage Assessment Using a
Generalized Minimum Rank Perturbation Theory”
Samuel, P. D., Pines, D. J., 2004, “A Review of Vibration-based Techniques
for Helicopter Transmission Diagnostics”
Sheinman, I., 1996, “Damage Detection and Updating of Stiffness and Mass
Matrices using Mode Data”
Tsuei, Y. G., Yee, E. K. L., 1989, “A Method for Modifying Dynamic
Properties of Undamped Mechanical Systems”
Zimmerman, D. C., Kaouk, M., 2005, “Model Correlation and System Health
Monitoring using Frequency Domain Measurements”
© D. Adams 2006
Journal: Applied a sensitivity approach that used both time and frequency to
localize damage in a finite element beam model.
Journal: Eigenmodes and static displacements were used to detect changes in
stiffness.
Journal: Modeled damage using changes in the component stiffness matrix
and treated the damage detection problem as a minimization problem.
Journal: Found that changes in environmental factors contribute less
significantly to the structural natural frequencies than actual damage.
Journal: Addressed unsymmetric impedance matrices with singular value
decomposition to acquire a damage vector.
Journal: Points out progress in the area of vibration-based fault detection.
Journal: Damage was detected using minimal static and dynamic
measurements through a closed form algorithm.
Journal: Modified mass and stiffness matrices by adding small changes in
mass and stiffness to the forcing function of the unmodified structure.
Journal: Addressed unsymmetric impedance matrices with singular value
decomposition to acquire a damage vector.
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: 19962001,” 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 5 th 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. 446451.
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.
1.
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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 14 th 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.
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© D. Adams 2006
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.
32. Worden, K. and Fieller, N.R.J., “Damage Detection Using Outlier Analysis,” 1999, Journal of Sound and Vibration, 229(3), pp.647-667.
33. Salawu, O.S., “Detection of Structural Damage through Changes in Frequency: A Review,” 1997, Engineering Structures, Vol. 19, No. 9, pp. 718-723.
34. Farrar, C.R., Doebling, S.W., Cornwell, P.J., and Straser, E.G., “Variability of Modal Parameters on the Alamosa Canyon Bridge,” 1997, Proc. 15th
International Modal Analysis Conference, Orlando, FL, pp. 257-263.
35. Doebling, S.W., Farrar, C.R., and Goodman, E.S., “Effects of Measurements Statistics on the Detection of Damage in the Alamosa Canyon Bridge,” 1997,
Proc. 15th International Modal Analysis Conference, Orlando, FL, pp. 919-929.
36. Cawley, P., and Adams, R.D., “Location of Defects in Structures from Measurements of Natural Frequencies,” 1979, Journal of Strain for Engineering
Design, Vol. 14, No. 2, pp. 49-57.
37. Pandey, A.K., Biswas, M., and Samman, M.M., “Damage Detection from Changes in Curvature Mode Shapes,” 1991, Journal of Sound and Vibration, Vol.
145, No. 2, pp. 321-332.
38. Pandey, A.K. and Biswas, M., “Damage Detection in Structures Using Changes in Flexibility,” 1994, Journal of Sound and Vibration, Vol. 169, No.1, pp. 317.
39. Pandey, A.K. and Biswas, M., “Damage Diagnosis of Truss Structures by Estimation of Flexibility Change,” 1995, Modal Analysis – The International
Journal of Analytical and Experimental Modal Analysis, Vol. 10, No. 2, pp. 104-117.
40. Lim, T.W., “Structural Damage Detection Using Modal Test Data,” 1991, AIAA Journal, Vol. 29, No. 12, pp. 2271-2274.
41. Lew, J.-S., “Using Transfer Function Parameter Changes for Damage Detection of Structures,” 1995 AIAA Journal, 33(11):2189-2193.
42. Banks, H. T., Inman, D. J., Leo, D. J., Want, Y., “An Experimentally Validated Damage Detection Theory in Smart Structures,” 1996, Journal of Sound and
Vibration 191 (5), pp. 2615-2621.
43. Doebling, S. W., “Minimum-Rank Optimal Update of Elemental Stiffness Parameters for Structural Damage Identification,” 1996, AIAA Journal 34 (12), pp.
2615-2621.
44. Escobar, J. A., Sosa, J. J., Gomez, R., “Structural Damage Detection using the Transformation Matrix,” 2005, Computers and Structures 83, pp. 357-368.
45. Fritzen, C. P., Jennewein, D., Kiefer, T., “Damage Detection Based on Model Updating Methods,” 1998, Mechanical Systems and Signal Processing 12 (1),
pp. 163-186.
46. Hajela, P. and Soeiro, F. J., “Structural Damage Detection Based on Static and Modal Analysis,” 1989, AIAA Journal 28 (6), pp. 1110-1115.
47. Hwang, H.Y., Kim C., “Damage detection using a few frequency response measurements,” 2004, Journal of Sound and Vibration 270, pp. 1-14.
48. Lew, J. S., “Using Transfer Function Parameter Changes for Damage Detection of Structures,” 1995, AIAA Journal 33 (11), pp. 2189-2193.
49. Kaouk, M., Zimmerman, D. C., “Structural Damage Assessment Using a Generalized Minimum Rank Perturbation Theory,” 1994, AIAA Journal 32 (4), pp.
836-842.
50. Samuel, P. D., Pines, D. J., “A Review of Vibration-based Techniques for Helicopter Transmission Diagnostics,” 2004, Journal of Sound and Vibration 282,
pp. 475-508.
51. Sheinman, I., “Damage Detection and Updating of Stiffness and Mass Matrices using Mode Data,” 1996, Computers & Structures 59 (1), pp. 149-156.
52. Tsuei, Y. G., Yee, E. K. L., “A Method for Modifying Dynamic Properties of Undamped Mechanical Systems,” 1989, Dynamic System Measurement
Control 111, pp. 403-408.
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© D. Adams 2006
53. Zimmerman, D. C., Kaouk, M., “Model Correlation and System Health Monitoring using Frequency Domain Measurements,” 2005, Structural Health
Monitoring 4 (3), pp. 213-215.
Table B.13 – References on wave propagation for damage identification.
Reference
Doebling et al., 1996, “Damage Identification and Health Monitoring of
Structural and Mechanical Systems from Changes in Their Vibration
Characteristics: A Literature Review”
Sohn et al., 2001, “A Review of Structural Health Monitoring Literature: 19962001”
Kessler, 2002, “Piezoelectric-Based In-Situ Damage Detection of Composite
Materials for Structural Health Monitoring Systems”
Wilcox et al, 1999, “Mode Selection and Transduction for Structural
Monitoring Using Lamb Waves”
Bar-Cohen et al., 1998, “Composite Material Defects Characterization Using
Leaky Lamb wave Dispersion Data”
Grisso, 2004, “Considerations of the Impedance Method, Wave Propagation,
and Wireless Systems for Structural Health Monitoring”
Lakshmanan and Pines, 1997, “Modeling Damage in Rotorcraft Flexbeams
using Wave Mechanics”
Pines, 1997, “The Use of Wave Propagation Models for Structural Damage
Identification”
Prosser et al, 1995, “Advanced, Waveform Based Acoustic Emission Detection
of Matrix Cracking in Composites”
Wevers, 1997, “Listening to the Sound of Materials: Acoustic Emission for the
Analysis of Material Behavior”
Shah et al, 2000, “New Directions in Concrete Health Monitoring Technology”
Adamou, and Craster, 2004, “Spectral Methods for Modeling Guided Waves in
Elastic Media”
Alleyne, and Cawley, 1992a, “The Interaction of Lamb Waves with Defects”
Alleyne, and Cawley, 1992b, “Optimization of Lamb Wave Inspection
Techniques”
Beard, 2002, “Guided Wave Inspection of Embedded Cylindrical Structures”
DRAFT
Summary
Report: Includes a review of literature on damage identification using
propagating elastic waves.
Report: Includes a review of literature on damage identification using
propagating elastic waves.
Thesis: Damage identification using guided waves on an Al plate and
composite cylinder. Literature review of guided waves.
Conference: Developed mode selection and transduction rules for monitoring
structures using Lamb waves.
Conference: Monitored the changes in dispersion characteristics of a leaky
Lamb wave to characterize porosity in a composite plate.
Thesis: Studied temperature influences on wave propagation. Presented a
method to quantify damage using the impedance method.
Journal: Used and developed a wave propagation method to identify
delaminations and transverse cracks in Gr/Ep composite rotorcraft.
Conference: Identified damage in beams using wave propagation by modeling
damage as a local change in dispersion; local and global defects.
Journal: Used acoustic emission to identify cracking of thin composite
specimens; also outlined the difficulties associated with acoustic emission.
Journal: Outlined the advantages of acoustic emission techniques over other
NDE methods for identifying damage in a loaded composite component.
Journal: Used stress waves (0-100 kHz) and found that changes in signal
amplitude across a crack were sensitive to crack.
Journal: Spectral method for dispersion curve generation of inhomogeneous,
curved, multilayered and materially damped structures.
Journal: Numerical and experimental study of defect identification using
Lamb waves and two-dimensional fast Fourier transforms.
Journal: Tests conducted on a butt-welded steel plate using A1 mode Lamb
wave.
Thesis: Detailed literature review and numerical development of guided wave
inspection of curved plates and cylindrical structures.
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Banerjee et al, 2003, “Lamb Wave Propagation and Scattering in Layered
Composite Plates”
Bar Cohen, 2000, “Emerging NDE Technologies and Challenges at the
Beginning of the 3rd Millennium - Part I”
Mustafa et al., 1997, “Imaging of Disbond in Adhesive Joints with Lamb
Waves”
Chahbaz, et al., 1996, “Corrosion Detection in Aircraft Structures using
Guided Lamb Waves”
Fromme, 2001, “Defect Detection in Plates using Guided Waves”
Giurgiutiu, 2003, “Lamb Wave Generation with Piezoelectric Wafer Active
Sensors for Structural Health Monitoring”
Lamb, 1917, “On Waves in An Elastic Plate”
Lord-Rayleigh, 1889, “On the Free Vibrations of An Infinite Plate of
Homogeneous Isotropic Matter”
Lowe, 1995, “Matrix Techniques for Modeling Ultrasonic Waves in
Multilayered Media”
Pavlakovic et al, 1997, “Disperse: A General Purpose Program for Creating
Dispersion Curves”
Pavlakovic, 1998, “Leaky Guided Ultrasonic Waves in NDT”
Pavlakovic, and Lowe, 1999, “A General Purpose Approach to Calculating the
Longitudinal and Flexural Modes of Multi-layered, Embedded, Transversely
Isotropic Cylinders”
Purekar, and Pines, 2002, “A Phased Sensor/Actuator Array for Detecting
Damage in 2-d Structures”
Purekar and Pines, 2005, Damage Detection in Plate Structures Using Lamb
Waves with Directional Filtering Sensor Arrays”
Raghavan and Cessnik, 2005, , “Piezoelectric-Actuator Excited-Wavefield
Solutions for Guided-Wave Structural Health Monitoring”
Rose, 1999, “Ultrasonic Waves in Solid Media”
Schmerr Jr., 1998, “Fundamentals of Ultrasonic Nondestructive Evaluation: A
Modeling Approach”
Sohn et al, 2004, “Multi-Scale Structural Health Monitoring for Composite
DRAFT
© D. Adams 2006
Conference: Lamb waves for crack identification in composite plates.
Journal: Traditional NDE techniques (ultrasonics, radiography, shearography)
and associated challenges are reviewed.
Online Journal: Detect and image disbonds in the tear-strap by using angle
wedge transducers to excite select Lamb modes.
Online Journal: Demonstrated the use of Lamb waves to detect corrosion
damage in an aluminum fuselage panel.
Thesis: Studied and compared scatter patterns of the antisymmetric Lamb
wave mode using both experimental and analytical results.
Conference: Used piezoelectric sensors for detecting damage in an aluminum
plate.
Journal: The first work dealing with guided wave propagation in thin elastic
specimens.
Journal: The first work dealing with wave propagation in a semi-infinite
solid.
Journal: Literature review of work involving guided wave dispersion curve
generation.
Conference: Outlines the software developed by researchers at Imperial
College for generating guided wave dispersion curves and mode shapes.
Thesis: Provided design rules for generating Lamb waves; also carried out
defect identification studies in plates and shells.
Conference: Outlined dispersion curve (longitudinal and flexural modes)
characterization in a composite cylinder.
Conference: Outlined phased arrays for damage identification in 2-d
structures; testing was carried out on aluminum beam and plate specimens.
Conference: Use of a directional filtering algorithm for defect localization in
structures.
Conference: Analytical development of arbitrary shaped piezoelectric
actuator to excite Ao and So mode Lamb waves from 3-D elasticity.
Book: A detailed outline of structural wave propagation with specific
emphasis on free and forced guided waves for NDE applications.
Book: A mathematical approach to ultrasonic nondestructive evaluation using
transfer functions including traditional ultrasonic testing methods.
Conference: Used Lamb waves to identify areas of delamination by
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Structures”
Sundararaman, 2003, “Structural Diagnostics through Beamforming of Phased
Arrays: Characterizing Damage in Steel and Composite Plates”
Tucker, 2001, “Ultrasonic Waves in Wood-based Composite Panels”
Viktorov, I.A., 1967, “Rayleigh and Lamb Waves: Physical Theory and
Applications”
Wilcox, 1998, “Lamb Wave Inspection of Large Structures using Permanently
Attached Transducers”
Worlton, 1961, “Experimental Confirmation of Lamb Waves at Megacycle
Frequencies”
Rizzo, and di Scalea, 2005, “Ultrasonic Inspection of Multi-wire Steel Strands
with the Aid of the Wavelet Transform”
Sundararaman et al, 2004a, “Incipient Damage Identification using Elastic
Wave Propagation through a Friction Stir Welded Al-Li Interface for
Cryogenic Tank Applications”
Sundararaman et al, 2004b, “Structural Health Monitoring Studies of a Friction
Stir Welded Al-Li Plate for Cryotank Application”
Purekar, and Pines, 2001, “Interrogation of Beam and Plate Structures Using
Phased Array Concepts”
Purekar et al, 2004, “Directional Piezoelectric Phased Array Filters for
Detecting Damage in Isotropic Plates”
Giurgiutiu, and Bao, 2002, “Embedded Ultrasonic Structural Radar with
Piezoelectric Wafer Active Sensors for the NDE of Thin-Wall Structures”
Yu, and Giurgiutiu, 2005, “Improvement of Damage Detection with the
Embedded Ultrasonics Structural Radar for Structural Health Monitoring”
Bardouillet, P., 1984, “Application of Electronic Focusing and Scanning
Systems to Ultrasonic Testing”
Ihn and Chang, 2004, “Detection and Monitoring of Hidden Fatigue Crack
Growth Using a Built-in Piezoelectric Sensor/Actuator Network: I.
Diagnostics”
MacLauchlan et al, 1998, “Phased Array EMATs for Flaw Sizing”
McNab, and Campbell, 1987, “Ultrasonic Phased Arrays for Nondestructive
Testing”
Sundararaman, and Adams, 2002, “Phased transducer arrays for Structural
DRAFT
© D. Adams 2006
implementing the ideas of time reversal acoustics.
Thesis: Outlined a phased array directional filtering algorithm for damage
localization in steel and woven composite structures.
Thesis: Includes a literature review of the use of ultrasonics in NDE.
Demonstrated defect identification in wood analytically and experimentally.
Book: Includes models for the generation of Lamb and Rayleigh waves using
ultrasonic transducers.
Thesis: Includes analytical and experimental development of piezoelectric
transducers for defect identification of large structures using Lamb waves.
Journal: One of the first works to identify the usefulness of Lamb waves for
NDE applications.
Journal: Used discrete wavelet transforms to filter (denoise) data and
compress data for feature extraction; applied to multi-wire steel strands.
Conference: Guided wave experimental investigation using acoustic emission
transducers and piezoelectric actuators.
Conference: Presented wavelet and statistical analysis techniques for defect
identification in a friction stir welded Al-Li plate.
Conference: Presented a phased array method using a sweep sine broadband
signal to identify damage in beam and plate structures.
Journal: A detailed numerical and experimental presentation of the phased
array method for defect localization in an aluminum plate.
Conference: A detailed experimental presentation for defect identification
using phased arrays consisting of piezoelectric wafers.
Conference: Presented new techniques for improving defect identification
using unitized phased arrays.
Journal: One of the early works to use ultrasonic phased arrays for detecting
defects in welds.
Journal: Used spectrograms to process guided wave signals obtained from an
array of piezoelectric transducers to detect and monitor fatigue crack growth.
Conference: Used phased array EMATs to generate and direct high frequency
shear horizontal (SH) waves for defect identification of weld samples.
Journal: Conducted a feasibility study (cost vs sample rate vs
instrumentation) for using ultrasonic phased arrays for NDE.
Conference: Developed a spatio-temporal directional filtering methodology
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Diagnostics Through Beamforming”
Sundararaman et al, 2005a, “Biologically Inspired Structural Diagnostics
through Beamforming with Phased Transducer Arrays”
Sundararaman et al, 2005b, “Structural Damage Identification in
Homogeneous and Heterogeneous Structures Using Beamforming”
Tua et al, 2004, “Detection of Cracks in Plates using Piezo-actuated Lamb
Waves”
Li and Rose, 2001, “Implementing Guided Wave Mode Control by use of a
Phased Transducer Array”
Lin, 2000, “Structural Health Monitoring using Geophysical Migration
Technique with Built-in Piezoelectric Sensor/Actuator Arrays”
Lin and Yuan, 2001, “Diagnostic Lamb Waves in an Integrated Piezoelectric
Sensor/Actuator Plate: Analytical and Experimental Studies”
Wang, 2004, “Elastic Wave Propagation in Composites and Least-Squares
Damage Localization Technique”
Wang, and Yuan, 2005, “Damage Identification in a Composite Plate using
Prestack Reverse-time Migration Technique”
Wilcox et al, 2001, “The Effect of Dispersion on Long-range Inspection using
Ultrasonic Guided Waves”
Wilcox et al, 2000, “Lamb and SH Wave Transducer Arrays for the Inspection
of Large Areas of Thick Plates”
Wilcox, 2003, “A Rapid Signal Processing Technique to Remove the Effect of
Dispersion from Guided Wave Signals”
Wilcox, 2003, “Omni-Direct ional Guided Wave Transducer Arrays for the
Rapid Inspection of Large Areas of Plate Structures”
Wilcox et al, 2005, “Omnidirectional Guided Wave Inspection of Large
Metallic Plate Structures Using an EMAT Array”
Rajagopalan et al, 2006, “A Phase Reconstruction Algorithm for Lamb Wave
Based Structural Health Monitoring of Anisotropic Multilayered Composite
Plates”
Chen et al, 2003, “Acoustic Emission in Monitoring Quality of Weld in
Friction Stir Welding”
Lamarre and Moles, 2000, “Ultrasound Phased Array Inspection Technology
for the Evaluation of Friction Stir Welds”
Raghavan and Cessnik, 2007, “Guided-wave Based Structural Health
Monitoring: A Review”
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for defect localization in isotropic structures.
Journal: Presented an experimental study for directional filtering using
antisymmetric (Ao) mode Lamb waves in steel and woven composites.
Journal: Presented an experimental study for directional filtering using
antisymmetric (Ao) mode Lamb waves in steel and woven composites.
Journal: Used the Hilbert Huang transform to detect cracks in plates
interrogated by piezo-actuated Lamb waves.
Journal: Use of guided waves for inspection of long pipes with a phased
transducer array.
Thesis: Presented a NDE technique based on ultrasonic sensor arrays using
the ideas of geophysical migration.
Journal: Modeled guided waves in an infinite isotropic plate (incorporating
Mindlin plate theory) using a pair of circular actuators.
Thesis: Used a least squares approach with iterative minimization for damage
localization using distributed arrays.
Journal: A pre-stack migration technique was used to locate damage in
composite structures.
Journal: Studied the effects of dispersion and mode sensitivity for defect
identification in order to develop design guidelines for guided wave testing.
Conference: Presented a method of using antisymmetric Lamb and shear
horizontal waves for defect identification over large areas of thick plates.
Journal: Used the symmetric (So) mode Lamb wave and attempted to
compensate for signal dilation due to dispersion.
Journal: Incorporated a dispersion compensation technique and developed a
guided wave compact phased transducer technique; holes and notches.
Journal: Extended the work to using an EMAT array for defect identification
in large metallic structures.
Journal: Extended the work by Wilcox (2003b) to locate damage (medium
sized through hole) using a single actuator and multiple sensors.
Conference: Used acoustic emission techniques for monitoring the quality of
welds obtained through the friction stir welding process.
Conference: Identified defects in a friction stir weld using ultrasonic phased
arrays.
Journal: A detailed review paper on work involving the use of guided waves
for nondestructive testing.
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Kundu et al, 2001, “Importance of the Near Lamb Mode Imaging of
Multilayered Composite Plates”
Crawley and de Luis, 1987, “Use of Piezoelectric Actuators as Elements of
Intelligent Structures”
Yang, J., and Chang, F., 2006, “Detection of Bolt Loosening in C-C Composite
Thermal Protection Panels: I. Diagnostic Principle”
1.
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Journal: Showed that it was possible to detect internal defects in layers of
mirror symmetry in the upper and lower halves of a plate.
Journal: Proposed a quasi-static induced strain actuation piezo actuator model
that can be more effectively modeled to operate in a pinching mode.
Journal: Used elastic waves to determine the preload in bolt connections of
thermal protection panels.
Adamou, A.T.I., and Craster, R.V., “Spectral Methods for Modeling Guided Waves in Elastic Media,” 2004, Journal of the Acoustical Society of
America, Vol. 116, No.3, pp. 1524-1535.
Alleyne, D.N., and Cawley, P., “The Interaction of Lamb Waves with Defects,” 1992a, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency
Control, Vol. 39, No. 3, pp. 381-397.
Alleyne, D.N., and Cawley, P., “Optimization of Lamb Wave Inspection Techniques,” 1992b, NDT and E International, Vol. 25, pp. 11–22.
Banerjee, S., Banerji, P., Berning, F., and Eberle, K., “Lamb Wave Propagation and Scattering in Layered Composite Plates,” 2003, Proc. of SPIE,
Smart NDE for Health Monitoring of Structural and Biological Systems, 8th Annual International Symposium on NDE for Health Monitoring &
Diagnostics, San Diego, California, Paper No. 5047-02.
Bar-Cohen, Y., “Emerging NDE Technologies and Challenges at the Beginning of the 3rd Millennium - Part I,” 2000, Materials Evaluation, Vol. 58, No.
1, pp. 17-30.
Bar-Cohen, Y., Mal, A., and Chang, Z., “Composite Material defects Characterization Using Leaky Lamb wave Dispersion Data,” 1998, Proc. of SPIE,
NDE Techniques for Aging Infrastructure & Manufacturing, Conference NDE of Materials and Composites II, San Antonio, Texas, Vol. 3396, Paper
No. 3396-25.
Bardouillet, P., “Application of Electronic Focusing and Scanning Systems to Ultrasonic Testing,” 1984, NDT International, Vol. 17, No. 2, pp. 81- 85.
Beard, M.D., “Guided Wave Inspection of Embedded Cylindrical Structures,” 2002, PhD Dissertation, University of London.
Chahbaz, A., Mustafa, V., and Hay, D.R., “Corrosion Detection in Aircraft Structures using Guided Lamb Waves,” 1996,
http://www.ndt.net/article/tektrend/tektrend.htm, Vol. 1, No.11, Online Journal.
Chen, C., Kovacevic, R., and Jandgric, D., “Acoustic Emission in Monitoring Quality of Weld in Friction Stir Welding,” 2003, Proc. of the Fourth
International Symposium on Friction Stir Welding, Park City, Utah, USA, 14-16 May 2003.
Crawley E.F. and de Luis J., “Use of Piezoelectric Actuators as Elements of Intelligent Structures,” 1987, AIAA Journal, Vol. 25, No. 10, pp.1373-1385,
Oct 1987
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.
Fromme, P., “Defect Detection in Plates Using Guided Waves,” 2001, Doctoral Dissertation, Swiss Federal Institute of Technology, Zurich. Eth: 14397.
Giurgiutiu, V. and Bao, J., “Embedded-Ultrasonics Structural Radar for In-Situ Structural Health Monitoring of Thin-Wall Structures,” 2004, Structural
Health Monitoring – an International Journal, Vol. 3, Number 2, June 2004, pp. 121-140.
Giurgiutiu, V., “Lamb Wave Generation with Piezoelectric Wafer Active Sensors for Structural Health Monitoring,” 2003, Proc. of the SPIE 5056, pp.
111–122.
Giurgiutiu, V., and Bao, J., “Embedded Ultrasonic Structural Radar with Piezoelectric Wafer Active Sensors for the NDE of Thin-Wall Structures,”
2002, Proc. of ASME International Mechanical Engineering Congress, Nov. 17-22, New Orleans, LA, CDROM, paper # IMECE 2002-39017, p. 1-8.
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17. Grisso, B.L., “Considerations of the Impedance Method, Wave Propagation, and Wireless Systems for Structural Health Monitoring,” 2004, MS Thesis,
Virginia Polytechnic Institute and State University.
18. Ihn, J.-B., and Chang, F.-K., “Detection and Monitoring of Hidden Fatigue Crack Growth Using a Built-in Piezoelectric Sensor/Actuator Network: I.
Diagnostics,” 2004, Smart Materials and Structures, Vol. 13, pp. 609-620.
19. Kessler, S. S., “Piezoelectric-Based In-Situ Damage Detection of Composite Materials for Structural Health Monitoring Systems,” 2002, Ph.D.
Dissertation, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology.
20. Kundu, T., Potel, C., and de Belleval, J.F., “Importance of the Near Lamb Mode Imaging of Multilayered Composite Plates,” 2001, Ultrasonics, vol. 39,
pp. 283-290.
21. Lakshmanan, K.A., and Pines, D.J., “Modeling Damage in Rotorcraft Flexbeams using Wave Mechanics,” 1997, Smart Materials and Structures, Vol.6,
pp. 383-392.
22. Lamarre, A., and Moles, M., “Ultrasound Phased Array Inspection Technology for the Evaluation of Friction Stir Welds,” 2000, Annual Conference of
the British Institute of Non-Destructive Testing Proceedings, pp. 56-61.
23. Lamb. H., “On Waves in an Elastic Plate,” 1917, Proc. of the Royal Society, London, Vol. 93, pp.114–128.
24. Li, J., and Rose, J. L., “Implementing Guided Wave Mode Control by use of a Phased Transducer Array,” 2001, IEEE Transactions on Ultrasonics,
Ferroelectrics, and Frequency Control, Vol. 48, No. 3, pp. 761-768.
25. Lin X. and Yuan F. G., “Diagnostic Lamb Waves in an Integrated Piezoelectric Sensor/Actuator Plate: Analytical and Experimental Studies,” 2001,
Smart Materials and Structures, Vol. 10, pp. 907–913.
26. Lin, X., “Structural Health Monitoring using Geophysical Migration Technique with Built-in Piezoelectric Sensor/Actuator Arrays,” 2000, PhD
Dissertation, North Carolina State University.
27. Liu, W., “Multiple Wave Scattering and Calculated Effective Stiffness and Wave Properties in Unidirectional Fiber-Reinforced Composites,” 1997,
PhD. Dissertation, Engineering Mechanics, Virginia Polytechnic.
28. Lord-Rayleigh, “On the Free Vibrations of an Infinite Plate of Homogeneous Isotropic Matter,” 1889, Proc. of the London Mathematical Society, Vol.
20, pp.225–234.
29. Lowe, M.J.S., “Matrix Techniques for Modeling Ultrasonic Waves in Multilayered Media,” 1995, IEEE Transactions on Ultrasonics, Ferroelectrics and
Frequency Control, Vol. 42, pp.525–542.
30. Lui, G., and Qu, J., “Guided Circumferential Waves in a Circular Annulus,” 1998, Journal of Applied Mechanics, vol.65, pp.424-430.
31. MacLauchlan, D.T., Schlader, D.M., Clark, S.P., and Latham, W.M., “Phased Array EMATs for Flaw Sizing,” 1998, EPRI Phased Array Inspection
Seminar 99-01, Portland, Maine.
32. McNab, A., and Campbell, M.J., “Ultrasonic Phased Arrays for Nondestructive Testing,” 1987, NDT International, Vol. 6, pp. 333-337.
33. Mustafa, V., Chahbaz, A., Hay, D.R., Brassard, M., and Dubois, S., “Imaging of Disbond in Adhesive Joints with Lamb Waves,” 1997,
http://www.ndt.net/article/tektren2 /tektren2.htm, Vol. 2, No. 3, Online Journal.
34. Pavlakovic, B., “Leaky Guided Ultrasonic Waves in NDT,” 1998, Doctoral Dissertation, Imperial College, University of London.
35. Pavlakovic, B., and Lowe, M.J.S., “A General Purpose Approach to Calculating the Longitudinal and Flexural Modes of Multi-Layered, Embedded,
Transversely Isotropic Cylinders,” 1999, Review of Progress in Quantitative Nondestructive Evaluation, D. O. Thompson and D. E. Chimenti, editors,
Vol. 18A, pp. 239–246, New York: Plenum Press.
36. Pavlakovic, B., Lowe, M.J.S., Alleyne, D., and Cawley, P., “Disperse: A General Purpose Program for Creating Dispersion Curves,” 1997, Review of
Progress in Quantitative Nondestructive Evaluation, D. O. Thompson and D. E. Chimenti, editors, Vol. 16A, pp. 185–192, New York: Plenum Press.
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37. Pines, D.J., “The Use of Wave Propagation Models for Structural Damage Identification”, 1997, Structural Health Monitoring: Current Status and
Perspectives, International Workshop on Structural Health Monitoring, Stanford CA, 1997, Chang, F.-K., ed., Boca Raton, Florida: CRC Press Inc.,
pp.664-677.
38. Prosser, W.H., Jackson, K.E., Kellas, S., Smith, B.T., McKeon, J., and Friedman, A., “Advanced, Waveform Based Acoustic Emission Detection of
Matrix Cracking in Composites,” 1995, Materials Evaluation, Vol. 53, No. 9, pp. 1052-1058.
39. Purekar, A.S., and Pines, D.J., Damage Detection in Plate Structures Using Lamb Waves with Directional Filtering Sensor Arrays,” 2005, Proc. of the
Fifth International Workshop on Structural Health Monitoring, Stanford, CA, pp. 1025-1032.
40. Purekar, A.S., and Pines D.J., “A Phased Sensor/Actuator Array for Detecting Damage in 2-D Structures,” 2002, AIAA/ASME/ASCE/AHS/ASC
Structures, Structural Dynamics, and Materials Conf. (No 2002-1547), p. 1-9.
41. Purekar, A.S., and Pines, D.J., “Interrogation of Beam and Plate Structures Using Phased Array Concepts,” 2001, Proc. of the 12th International
Conference on Adaptive Structures and Technologies (ICAST), University of Maryland, MD, pp. 275-288.
42. Purekar, A.S., Pines, D.J., Sundararaman, S., and Adams, D.E., “Directional Piezoelectric Phased Array Filters for Detecting Damage in Isotropic
Plates,” 2004, Smart Materials and Structures, Vol. 13, pp. 838-850.
43. Raghavan, A., and Cesnik, C.E.S., “Piezoelectric-Actuator Excited-Wavefield Solutions for Guided-Wave Structural Health Monitoring,” 2005, Proc. of
the SPIE 5765, p. 1-11.
44. Rajagopalan, J., Balasubramanian, K., and Krishnamurthy, C.V., “A Phase Reconstruction Algorithm for Lamb Wave Based Structural Health
Monitoring of Anisotropic Multilayered Composite Plates,” 2006, Journal of the Acoustical Society of America, Vol. 119, No. 2, pp. 872-878.
45. Rizzo, P., and di Scalea, F.L., “Ultrasonic Inspection of Multi-wire Steel Strands with the Aid of the Wavelet Transform,” 2005, Smart Materials and
Structures, Vol. 14, pp. 685-695.
46. Rose, J.L., “Ultrasonic Waves in Solid Media,” 1999, London: Cambridge University Press.
47. Saravanos, D.A., and Heyliger, P.R., “Coupled Layerwise Analysis of Composite Beams with Embedded Piezoelectric Sensors and Actuators,” 1995,
Journal of Intelligent Material Systems and Structures, Vol.6, pp. 350-363.
48. Schmerr Jr., L.W., “Fundamentals of Ultrasonic Nondestructive Evaluation: A Modeling Approach,” 1999, New York: Plenum Press.
49. Shah, S. P., Popovics, J. S., Subramaniam, K. V., and Aldea, C., “New Directions in Concrete Health Monitoring Technology,” 2000, Journal of
Engineering Mechanics, Vol. 126, No. 7, pp. 754-760.
50. Sohn, H., Farrar, C.R., Hemez, F.M., Shunk, D.D., Stinemates, D.W., and Nadler, B.R., 2001, “A Review of Structural Health Monitoring Literature:
1996-2001,” Los Alamos National Laboratory Report LA-13976-MS.
51. Sohn, H., Wait, J.R., Park, G., and Farrar, C.R., “Multi-Scale Structural Health Monitoring for Composite Structures,” 2004, Proc. of the Second
European Workshop on Structural Health Monitoring, July 7-9, Munich, Germany, pp. 721-729.
52. Sundararaman, S., “Structural Diagnostics through Beamforming of Phased Arrays: Characterizing Damage in Steel and Composite Plates,” 2003, MS
Thesis, Purdue University.
53. Sundararaman, S., Adams, D.E., and Jata, K.V., “Structural Health Monitoring Studies of a Friction Stir Welded Al-Li Plate for Cryotank Application,”
2004b, Materials Damage Prognosis, Edited by TMS (The Minerals, Metals and Materials Society).
54. Sundararaman, S., Adams, D.E., and Rigas, E., “Biologically Inspired Structural Diagnostics through Beamforming with Phased Transducer Arrays,”
2005a, International Journal of Engineering Science, May 2005, pp. 756-778.
55. Sundararaman, S., Adams, D.E., and Rigas, E.J., “Structural Damage Identification in Homogeneous and Heterogeneous Structures Using
Beamforming,” 2005b, Structural Health Monitoring-an International Journal, pp. 171-190.
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56. Sundararaman, S., and Adams, D.E., “Phased Transducer Arrays for Structural Diagnostics Through Beamforming,” 2002, Proc. of the American
Society for Composites (ASC) 17th Technical Conference, W. Lafayette, IN, C.T. Sun and H. Kim eds., CD-ROM, Paper 177.
57. Sundararaman, S., Haroon, M., Adams, D.E., and Jata, K.V., “Incipient Damage Identification Using Elastic Wave Propagation through a Friction Stir
Welded Al-Li Interface for Cryogenic Tank Applications,” 2004a, Proc. of the Second European Workshop of Structural Health Monitoring, Munich,
Germany, DESTech Publications Inc., PA, USA, pp. 525-532.
58. Tua, P.S., Quek, S.T., and Wang, Q., “Detection of Cracks in Plates using Piezo-actuated Lamb Waves,” 2004, Smart Materials and Structures, Vol. 13,
pp. 643-660.
59. Tucker, B.J., “Ultrasonic Waves in Wood-based Composite Panels,” 2001, PhD Dissertation, Department of Civil and Environmental Engineering,
Washington State University.
60. Viktorov, I.A., “Rayleigh and Lamb Waves: Physical Theory and Applications,” 1967, New York: Plenum Press.
61. Wang, L., “Elastic Wave Propagation in Composites and Least-Squares Damage Localization Technique,” 2004, MS Thesis, North Carolina State
University, Rayleigh.
62. Wang, L., and Yuan, F.G., “Damage Identification in a Composite Plate using Prestack Reverse-time Migration Technique,” 2005, Structural Health
Monitoring – an International Journal, Vol. 4, No. 3, pp. 195-217.
63. Wevers, M., “Listening to the Sound of Materials: Acoustic Emission for the Analysis of Material Behavior,” 1997, NDT&E International, Vol. 30, No.
2, pp. 99-106.
64. Wilcox, P., Lowe, M., and Cawley, P., “Omnidirectional Guided Wave Inspection of Large Metallic Plate Structures Using an EMAT Array,” 2005,
IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, Vol. 52, No. 4, pp. 653-665.
65. Wilcox, P., Lowe, M., Cawley, P., “Lamb and SH Wave Transducer Arrays for the Inspection of Large Areas of Thick Plates,” 2000, Review of
Progress in Quantitative Nondestructive Evaluation, ed. D.O. Thomson and D.E. Chimenti, CP509, Vol. 18A, pp. 1049-1056.
66. Wilcox, P.D., “Lamb Wave Inspection of Large Structures using Permanently Attached Transducers,” 1998, PhD Dissertation, Imperial College of
Science Technology and Medicine, University of London.
67. Wilcox, P.D., “A Rapid Signal Processing Technique to Remove the Effect of Dispersion from Guided Wave Signals,” 2003, IEEE Transactions on
Ultrasonics, Ferroelectrics and Frequency Control, Vol. 50, No. 4, pp. 419-427.
68. Wilcox, P.D., “Omni-Direct ional Guided Wave Transducer Arrays for the Rapid Inspection of Large Areas of Plate Structures,” 2003, IEEE
Transactions on Ultrasonics, Ferroelectrics and Frequency Control, Vol. 50, No. 4, pp. 699-709.
69. Wilcox, P.D., Dalton, R.P., Lowe, M.J.S., and Cawley, P., “Mode Selection and Transduction for Structural Monitoring Using Lamb Waves,” 1999,
Structural Health Monitoring 2000, 2nd International Workshop on Structural Health Monitoring, Stanford, CA, Chang, F.-K., ed., Boca Raton, FL:
CRC Press Inc., pp. 703-712.
70. Wilcox, P.D., Lowe, M., and Cawley, P., “The Effect of Dispersion on Long-range Inspection using Ultrasonic Guided Waves,” 2001, NDT&E
International, Vol. 34, pp. 1-9.
71. Worlton, D.C., “Experimental Confirmation of Lamb Waves at Megacycle Frequencies,” 1961, Journal of Applied Physics, Vol. 32, pp. 967-971.
72. Yu, L., and Giurgiutiu, V., “Improvement of Damage Detection with the Embedded Ultrasonics Structural Radar for Structural Health Monitoring,”
2005, Proc. of the Fifth International Workshop on Structural Health Monitoring, ed. Fu-kuo Chang, pp. 1081-1090.
73. Yang, J., and Chang, F., “Detection of Bolt Loosening in C-C Composite Thermal Protection Panels: I. Diagnostic principle,” 2006, Smart Materials
and Structures 15, pp. 581-590.
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Table B.14 – References on temporal data analysis.
Reference
Samuel and Pines, 2005, “A Review of Vibration-Based Techniques for
Helicopter Transmission Diagnostics”
Staszewski and Worden, 2004, “Signal Processing for Damage Detection”
Box et al, 1994, “Time Series Analysis: Forecasting and Control”
Castillo et al, 2005, “Extreme Value and Related Models with Applications in
Engineering and Science”
Montgomery, 2001, “Design and Analysis of Experiments”
McLachlan, 1992, “Discriminant Analysis and Statistical Pattern Recognition”
Webb, 2002, “Statistical Pattern Recognition”
Sohn et al, 2000, “Structural Health Monitoring using Statistical Process
Control”
Todd and Nichols, 2002, “Structural Damage Assessment Using Chaotic
Dynamic Interrogation”
Monaco et al, 2000, “Experimental and Numerical Activities on Damage
Detection Using Magnetostrictive Actuators and Statistical Analysis”
Worden et al, 2003, “Extreme Value Statistics for Damage Detection in
Mechanical Structures”
George et al, 2000, “Identifying Damage Sensitive Features using Nonlinear
Time Series and Bispectral Analysis”
Kantz and Schreiber, 1997, “Nonlinear Time Series Analysis”
Yu and Giurgiutiu, 2005, “Advanced Signal Processing for Enhanced Damage
Detection with Piezoelectric Wafer Active Sensors”
1.
2.
3.
4.
DRAFT
Summary
Journal: A detailed review paper on statistical techniques in conjunction with
signal processing for helicopter transmission diagnostics.
Book Chapter: Includes a summary of data analysis methods for damage
identification with illustrations of data compression and denoising.
Book: Detailed account of time series analysis methods including different
auto-regressive and moving average models.
Book: Implementation and mathematical background for extreme value and
reliability models.
Book: Illustrates methods of combining and analyzing data using
experimental design and hypothesis testing.
Book: Seminal work in using temporal/transformed temporal data for feature
extraction and discrimination using pattern recognition.
Book: Includes basic and advanced statistical tools used for feature extraction
and data/feature discrimination using pattern recognition.
Conference: Experimental investigation of statistical process control to
identify damage during a vibration experiment.
Conference: Used a single factor analysis-of-variance (ANOVA) with
Bonferroni confidence interval generation to as a damage sensitive feature.
Journal: Used a t-test to determine the effectiveness of damage indices
obtained from changes in the frequency response functions.
Report: Detailed report on unsupervised learning methods based on extreme
value statistical analysis using statistical process control.
Conference: Multivariate analysis method that compares groups of data by a
weighted linear combination known as the canonical variate analysis.
Book: Detailed review on nonlinear time series analysis methods.
Journal: Detailed literature review of recent works using temporal and
frequency domain methods.
Box, G., Jenkins, G.M., and Reinsel, G., “Time Series Analysis: Forecasting and Control,” 1994, Third Edition, Prentice-Hall, New Jersey.
Castillo, E., Hadi, A.S., Balakrishnan, N., Sarabia, J.M., “Extreme Value and Related Models with Applications in Engineering and Science,” 2005,
John Wiley and Sons Inc., New Jersey.
George, D., Hunter, N., Farrar, C.R., Deen, R., “Identifying Damage Sensitive Features using Nonlinear Time Series and Bispectral Analysis,” 2000,
Proc. of the 18th International Modal Analysis Conference, San Antonio, Texas, p. 1-7.
Kantz, H., Schreiber, T., “Nonlinear Time Series Analysis,” 1997, Cambridge Nonlinear Science Series 7, Cambridge University Press, Cambridge, UK.
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6.
7.
8.
9.
10.
11.
12.
13.
14.
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© D. Adams 2006
McLachlan, G.J., “Discriminant Analysis and Statistical Pattern Recognition,” 1992, John Wiley and Sons, New York.
Monaco, E., Franco, F., and Lecce, L., “Experimental and Numerical Activities on Damage Detection Using Magnetostrictive Actuators and Statistical
Analysis,” 2000, Journal of Intelligent Materials and Structures, Vol. 11, pp. 567-578.
Montgomery, D.C., “Design and Analysis of Experiments,” 2001, Fifth Edition, John Wiley and Sons, New York.
Samuel, P.D., and Pines, D.J., “A Review of Vibration-Based Techniques for Helicopter Transmission Diagnostics,” 2005, Journal of Sound and
Vibration, Vol. 282, pp. 475-508.
Sohn, H., Czarnecki, J.A., and Farrar, C.R., “Structural Health Monitoring Using Statistical Process Control,” 2000, Journal of Structural Engineering,
Nov. 2000, pp. 1356-1363.
Staszewski W. and Worden K., “Signal Processing for Damage Detection,” 2004, Health Monitoring of Aerospace Structures, eds. Staszewski W.,
Boller C. and Tomlinson G., John Wiley & Sons, UK, pp. 163-206.
Todd, M.D., and Nichols, J.M., “Structural Damage Assessment Using Chaotic Dynamic Interrogation,” 2002, Proc. of 2002 ASME International
Mechanical Engineering Conference and Exposition, v. 71, pp. 613-620.
Webb A., “Statistical Pattern Recognition,” 2002, Second Edition, John Wiley and Sons, West Sussex, UK.
Worden, K., Allen, D.W., Sohn, H., Stinemates, D.W., and Farrar, C.R., “Extreme Value Statistics for Damage Detection in Mechanical Structures,”
2003, Los Alamos National Laboratory Report LA-13903-MS.
Yu, L., and Giurgiutiu, V., “Advanced Signal Processing for Enhanced Damage Detection with Piezoelectric Wafer Active Sensors,” 2005, Smart
Systems and Structures, Vol. 1, No.2, pp. 185-215.
Table B.15 – References on time-frequency data analysis.
Reference
Staszewski, W.J., 1998, “Wavelet Based Compression and Feature Selection
for Vibration Analysis”
Prosser et al, 1999, “Time-Frequency Analysis of the Dispersion of Lamb
Modes”
Cao, X., 2002, “Adaptability and Comparison of the Wavelet-based with
Traditional Equivalent Linearization Method and Potential Application for
Damage Detection.”
Yuan et al, 2004, “A New Damage Signature for Composite Structural Health
Monitoring.”
Peng et al, 2005, “A Comparison Study of Improved Hilbert-Huang Transform
and Wavelet Transform: Application to Fault Diagnosis for Roller Bearing”
Shinde, 2004, “A Wavelet Packet Based Sifting Process and Its Application in
Structural Health Monitoring”
Cohen, 1995, “Time-Frequency Analysis”
Auger et al, 1996, “Time Frequency Toolbox – For Use with MATLAB:
DRAFT
Summary
Journal: Used wavelet analysis to extract features from vibration time series
to detect damage.
Journal: Lamb mode signals were processed using a pseudo Wigner Ville
distribution for determining material properties (i.e., dispersion).
Thesis: Presented background for time-frequency analysis and compared a
wavelet based equivalent linearization method with traditional method.
Journal: Introduced a damage signature based on wavelet analysis to
determine the presence and extent of damage.
Journal: Compared the results obtained by processing data using the Hilbert
Huang transform (HHT) and wavelet analysis.
Thesis: Extended the HHT by using wavelet packet principles; also included
details and background about obtaining the HHT and wavelet transform.
Book: Outline and mathematical background for time-frequency methods
used for signal analysis.
Online Report: Review article and tutorial in the use of time, frequency and
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Tutorial”
Huang et al, 1998, “The Empirical Mode Decomposition Method and the
Hilbert Spectrum for Non-linear and Non-stationary Time Series Analysis”
Daubechies, I., 1992, “Ten Lectures in Wavelets”
Daubechies, I., 1990, “The Wavelet Transform, Time-Frequency Localization
and Signal Analysis”
Donoho, D.L., 1995, “De-noising by Soft-Thresholding”
Jensen and la Cour-Harbo, 2001, “Ripples in Mathematics: The Discrete
Wavelet Transform”
Mallat, 1999, “A Wavelet Tour of Signal Processing”
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
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© D. Adams 2006
time-frequency analysis (including wavelet analysis) with MATLAB .
Journal: Detailed literature review of time frequency analysis and extends the
Hilbert transform by implementing empirical mode decomposition.
Journal & Book: Seminal works on wavelet analysis; used quadrature mirror
filters associated with the scaling function and the mother wavelet function.
Journal: Presented a soft thresholding method for denoising data using the
wavelet transform.
Book: Review, background and implementation of time-frequency analysis
(wavelet transforms).
Book: Review, background and implementation of time-frequency analysis
(wavelet transforms).
Auger, F., Flandrin, P., Goncalves, P., and Lemoine, O., “Time Frequency Toolbox – For Use with MATLAB: Tutorial,” 1996, Web: Matlab File
Exchange.
Cao, X., “Adaptability and Comparison of the Wavelet-based with Traditional Equivalent Linearization Method and Potential Application for Damage
Detection,” 2002, MS Thesis (Advisor: Mohammad N. Noori), North Carolina State University.
Cohen, L., “Time-Frequency Analysis,” Prentice Hall, Englewood Cliffs, NJ, 1995.
Daubechies, I., “Ten Lectures in Wavelets,” 1992, CBMS-NSF Regional Conference Series in Mathematics, Society for Industrial and Applied Math
(SIAM), Philadelphia, PA.
Daubechies, I., “The Wavelet Transform, Time-Frequency Localization and Signal Analysis,” 1990, IEEE Transactions on Information Theory, Vol. 36,
No. 5, pp. 961-1005.
Donoho, D.L., “De-noising by Soft-Thresholding,” 1995, IEEE Transactions on Information Theory, Vol. 41, No.3, pp. 613-627.
Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.-C., Tung, C.C., Liu, H.H., “The Empirical Mode Decomposition Method
and the Hilbert Spectrum for Non-linear and Non-stationary Time Series Analysis”, 1998, Proc. of the Royal Society London, Vol. 454, pp. 903-995.
Ihn, J.-B., and Chang, F.-K., “Detection and Monitoring of Hidden Fatigue Crack Growth Using a Built-in Piezoelectric Sensor/Actuator Network: I.
Diagnostics,” 2004, Smart Materials and Structures, Vol. 13, pp. 609-620.
Jensen, A., la Cour-Harbo, A., “Ripples in Mathematics: The Discrete Wavelet Transform,” 2001, Springer International, New Delhi.
Mallat, S., “A Wavelet Tour of Signal Processing,” 1999, Second Edition, Academic Press.
Peng, Z.K., Tse, P.W., Chu, F.L., “A Comparison Study of Improved Hilbert-Huang Transform and Wavelet Transform: Application to Fault Diagnosis
for Roller Bearing,” 2005, Mechanical Systems and Signal Processing, Vol. 19, pp. 974-988.
Prosser W.H., Seale M.D. and Smith B.T., “Time-Frequency Analysis of the Dispersion of Lamb Modes,” 1999, Journal of the Acoustical Society of
America, Vol. 105, No. 5, pp. 2669-2676.
Raghavan, A., and Cesnik, C.E.S., “Piezoelectric-Actuator Excited-Wavefield Solutions for Guided-Wave Structural Health Monitoring,” 2005, Proc. of
the SPIE 5765, p. 1-11.
Rizzo, P., and di Scalea, F.L., “Ultrasonic Inspection of Multi-wire Steel Strands with the Aid of the Wavelet Transform,” 2005, Smart Materials and
Structures, Vol. 14, pp. 685-695.
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15. Shinde, A.D., “A Wavelet Packet Based Sifting Process and Its Application in Structural Health Monitoring,” 2004, MS Thesis, Worcester Polytechnic
Institute.
16. Staszewski, W.J., “Wavelet Based Compression and Feature Selection for Vibration Analysis,” 1998, Journal of Sound and Vibration, v. 211(5), p. 735760.
17. Yuan, S., Wang, L., and Wang, X., “A New Damage Signature for Composite Structural Health Monitoring,” 2004, Proc. of the 2 nd European Workshop
on Structural Health Monitoring, Munich, Germany, July 7-9, 2004, p. 1-8.
18. Hou, Z., Noori S. and Amand, St. R., “A Wavelet-Based Approach for Structural Damage Detection”, 2000, ASCE Journal of Engineering Mechanics,
126, pp. 667-683.
Table B.16 – References on triangulation for damage location.
Reference
White et al, 2005, “Modeling and Material Damage Identification of a
Sandwich Plate Using MDOF Modal Parameter Estimation and the Method of
Virtual Forces”
Sundararaman, 2003, “Structural Diagnostics through Beamforming of Phased
Arrays: Characterizing Damage in Steel and Composite Plates”
1.
2.
Summary
Conference: Developed a distributed sensor array technique for detecting and
locating damage.
Thesis: Outlined a phased array directional filtering algorithm for damage
localization in steel and woven composite structures.
Sundararaman, S., “Structural Diagnostics through Beamforming of Phased Arrays: Characterizing Damage in Steel and Composite Plates,” 2003, MS
Thesis, Purdue University.
White, J., Adams, D.E., Jata, K.V., “Modeling and Material Damage Identification of a Sandwich Plate Using MDOF Modal Parameter Estimation and
the Method of Virtual Forces,” 2005, Proc. of the International Mechanical Engineers Congress and Exposition, Nov 5-11, 2005, Orlando, FL, Paper #:
80472.
Table B.17 – References on transfer path, other types of data analysis, and non-contact sensing.
Reference
Donskoy, D. et al., 2001, “Nonlinear Acoustic Interaction on Contact
Interfaces and Its Use for Nondestructive Testing”
Donskoy, D.M. et al., 1998, “Vibro-acoustic Modulation Nondestructive
Evaluation Technique”
Ballad, E.M. et al., 2004, “Nonlinear Modulation Technique for NDE with AirCoupled Ultrasound ”

R u z ek, R. et al., 2006, “ Ultrasonic C-Scan and Shearography NDI
Techniques Evaluation of Impact Defects Identification”
Edwards, R.S., et al., 2006, “Dual EMAT and PEC Non-Contact Probe:
Applications to Defect Testing”
Cho, H. et al., 1996, “Non-Contact Laser Ultrasonics for Detecting Subsurface
DRAFT
Summary
Journal: Used the modulation of a high-frequency ultrasonic wave by low
frequency vibration to detect defects.
Journal: Studied a new air-coupled nonlinear acoustic modulation method
that used non-contact ultrasound excitation.
Journal: Compared the ultrasonic C-scan with laser shearography method in
the impact damage identification of sandwich panels.
Journal: Applied a dual-probe combining electromagnetic acoustic
transducers and a pulsed eddy current sensor to detect defects.
Journal: Employed non-contact and non-destructive laser ultrasonics to
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Lateral Defects”
Warnemuende, K. et al., 2004, “Actively Modulated Acoustic Nondestructive
Evaluation of Concrete”
Moussatov, A. et al., 2002, “Frequency Up-Conversion and Frequency DownConversion of Acoustic Waves in Damaged Materials”
Li, T.Y. et al., 2004, “Vibrational Power Flow Characteristics of Circular Plate
Structures with Peripheral Surface Crack”
Sun, J.Q. , 1995, “Vibration and Sound Radiation of Non-Uniform Beams”
Lu, Y. et al., 2005, “A Methodology for Structural Health Monitoring with
Diffuse Ultrasonic Waves in the Presence of Temperature Variations”
Wevers, M., 1997, “Listening to the Sound of Materials: Acoustic Emission for
the Analysis of Material Behaviour”
Gudmundson, P., 1999, “Acoustic Emission and Dynamic Energy Release
Rate for Steady Growth of a Tunneling Crack in a Plate in Tension”
Toutountzakis, T. et al., 2003, “Observation of Acoustic Emission Activity
During Gear Defect Diagnosis”
Rippert, L. et al., 2000, “Optical and Acoustic Damage Detection in Laminated
CFRP Composite Materials”
Tong, F. et al., 2006, “Impact-Acoustics-Based Health Monitoring of Tile-Wall
Bonding Integrity Using Principal Component Analysis”
1.
2.
3.
4.
5.
6.
7.
DRAFT
© D. Adams 2006
identify subsurface lateral defects.
Journal: Studied nonlinear frequency analysis methods for concrete damage
detection and evaluation using actively modulated acoustic signals.
Journal: Investigated correlation between nonlinear signatures and amount of
damage.
Journal: Investigated the vibrational power flow of circular plates with a
surface crack.
Journal: Presented an analytical method for studying vibration and acoustic
radiation problems of non-uniform beams.
Journal: Applied diffuse ultrasonic waves to the problem of detecting
structural damage in the presence of unmeasured temperature changes.
Journal: Used acoustic emission to do detect damage in different types of
composite materials.
Journal: Studied acoustic emission and dynamic steady state growth of
tunneling cracks in membrane loaded isotropic Kirchhoff plates.
Journal: Applied acoustic emission as a non-destructive technique for
damage detection in rotating machinery.
Journal: Used an intensity-modulated fibre-optic sensor as an alternative to
the piezoelectric transducers for acoustic emission monitoring.
Journal: Used the impact-acoustic signature in tile-wall inspection to mitigate
the adverse influence of surface non-uniformity.
Donskoy, D., A. Sutin, A. Ekimov, “Nonlinear Acoustic Interaction on Contact Interfaces and Its Use for Nondestructive Testing,” 2001, NDT&E
international, 34:231-238.
Donskov, D., A. Sutin, “Vibro-Acoustic Modulation Nondestructive Evaluation Technique,” 1998, Journal of intelligent material systems and structures,
9(9):765-771.
Ballad, E. M., S. Yu. Vezirov, K. Pfleiderer, I. Yu. Solodov, G. Busse, “Nonlinear Modulation Technique for NDE with Air-Coupled Ultrasound.
Ultrasonics,” 2004, 42:1031-1036.

 z ek, Radek Lohonka, Josef Jiron c , “Ultrasonic C-Scan and Shearography NDI Techniques Evaluation of Impact Defects Identification,”
Roman R u
2006, NDT&E international, 39:132-142.
Edwards, R. S., A. Sophian, S. Dixon, G.-Y. Tian, X. Jian, “Dual EMAT and PEC Non-Contact Probe: Applications to Defect Testing,” 2006, NDT&E
international, 39:45-52.
Cho, H., Ogawa, S., and Takemoto, M., “Non-Contact Laser Ultrasonics for Detecting Subsurface Lateral Defects,” 1996, NDT&E international,
29(5):301-306.
Warnemuende, K., Hwai-Chung Wu, “Actively Modulated Acoustic Nondestructive Evaluation of Concrete,” 2004, Cement and concrete research,
34:563-570.
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9.
10.
11.
12.
13.
14.
15.
16.
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Moussatov, A., Bernard Castagn `e de, Vitalyi Gusev, “Frequency Up-Conversion and Frequency Down-Conversion of Acoustic Waves in Damaged
Materials,” 2002, Physics Letters A, 301:281-290.
Li, T. Y., J. X. Liu, T. Zhang, “Vibrational Power Flow Characteristics of Circular Plate Structures with Peripheral Surface Crack,” 2004, Journal of
sound and vibration, 276:1081-1091.
Sun, J., “Vibration and Sound Radiation of Non-Uniform Beams,” 1995, Journal of sound and vibration, 185(5):827-843.
Lu, Y., Jennifer E. Michaels, “A Methodology for Structural Health Monitoring with Diffuse Ultrasonic Waves in the Presence of Temperature
Variations,” 2005, Ultrasonics, 43:717-731.
Wevers, M., “Listening to the Sound of Materials: Acoustic Emission for the Analysis of Material Behaviour,” 1997, NDT&E international, 30(2):99106.
Gudmundson, P., “Acoustic Emission and Dynamic Energy Release Rate for Steady Growth of a Tunneling Crack in a Plate in Tension,” 1999, Journal
of the Mechanics and Physics of Solids, 47:2057-2074.
Toutountzakis, T., David Mba, “Observation of Acoustic Emission Activity during Gear Defect Diagnosis,” 2003, NDT&E international, 36:471-477.
Rippert, L., M. Wevers, S. Van Huffel, “Optical and Acoustic Damage Detection in Laminated CFRP Composite Materials,” 2000, Composites science
and technology, 60:2713-2724.
Tong, F., S. K. Tso, M.Y.Y. Hung, “Impact-Acoustics-Based Health Monitoring of Tile-Wall Bonding Integrity Using Principal Component Analysis,”
2006, Journal of Sound and Vibration, 294:329-340.
Table B.18 – References on variability analysis in health monitoring.
Reference
Lew, J.-S., 1995, “Using Transfer Function Parameter Changes for Damage
Detection of Structures”
Cornwell, P.J. et al., 1999, ” Environmental Variability of Modal Parameters”
Sohn, H. et al., 1998, “Adaptive Modeling of Environmental Effects in Modal
Parameters for Damage Detection in Civil Structures”
Peeters, B. et al., 2001, “Vibration-Based Damage Detection in Civil
Engineering: Excitation Sources and Temperature Effects”
Sohn, H. et al., 2003, “Statistical Damage Classification under Changing
Environmental and Operational Conditions”
Yan, A.-M. et al., 2005, “Structural Damage Diagnosis under Varying
Environmental Conditions - Part 1: A Linear Analysis”
Gawronski, W., 1999, “Simultaneous placement of actuators and sensors”
Shi, Y.Z. et al., 2000, “Optimum Sensor Placement for Structural Damage
Detection”
DRAFT
Summary
Journal: Developed an interval modeling technique to investigate how
environmental variations alter natural frequencies.
Journal: Investigated how temperature changes influence modal properties
using data from the Alamosa Canyon Bridge.
Conference: Applied an adaptive filter to establish a linear correlation
between temperature and natural frequencies.
Journal: Used a single-input single-output ARX model to fit baseline data
and then extrapolated the influence caused by thermal variations.
Journal: Showed that an AR-ARX model was able to detect damage in the
presence of wide operational and environmental ranges.
Journal: Uses principle component analysis to monitor systems under
varying environmental conditions.
Journal: Presents a sensor/actuator placement algorithm based on modal
norming.
Journal: Uses an eigenvector sensitivity analysis to eliminate potential sensor
locations.
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2.
3.
4.
5.
6.
7.
8.
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© D. Adams 2006
Lew, J.-S., “Using Transfer Function Parameter Changes for Damage Detection of Structures,” 1995, AIAA Journal, 33(11):2189-2193.
Cornwell, P. J., C. R. Farrar, S. W. Doebling, and H. Sohn, “Environmental Variability of Modal Parameters,” Experimental Techniques, 1999,
39(6):45-48.
Sohn, H., M. Dzwonczyk, E. G. Straser, K. H. Law, A. S. Kiremidjian, and T. Meng, “Adaptive Modeling of Environmental Effects in Modal
Parameters for Damage Detection in Civil Structures,” 1998, Proc. of SPIE - The International Society for Optical Engineering, 3325(1):127-138.
Peeters, B., J. Maeck, and G. De Roeck, “Vibration-Based Damage Detection in Civil Engineering: Excitation Sources and Temperature Effects,” 2001,
Smart Materials and Structures, 10(1):518-527.
Sohn, H., K. Worden, and C. R. Farrar, “Statistical Damage Classification under Changing Environmental and Operational Conditions,” 2003, Journal
of Intelligent Materials Systems and Structures, 13(9):561-574.
Yan, A.-M., G. Kerschen, P. De Boe, and J.-C. Golinval, “Structural Damage Diagnosis under Varying Environmental Conditions - Part 1: A Linear
Analysis,” 2005, Mechanical Systems and Signal Processing, 19(1):847-864.
Gawronski, W., “Simultaneous Placement of Actuators and Sensors,” 1999, Journal of Sound and Vibration, 228(4):915-922.
Shi, Z. Y., S. S. Law, and L. M. Zhang, “Optimum Sensor Placement for Structural Damage Detection,” 2000, Journal of Engineering Mechanics,
126(11):1173-1179.
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