Engineering Education Initiative
SE 265 Lecture 3January 17, 2006
Topics1. Review UC-Irvine Bridge Column Test2. Data Acquisition
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Last Time
• Discussed 4 predominant industries that have driven the development of SHM– Rotating Machinery– Off Shore Oil Platforms– Highway Bridge Structures– Aerospace Structure
• Discussed Operational Evaluation
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Operational Evaluation
• Operational evaluation begins to answer questions regarding implementation issues for a structural health monitoring system.– Provide economic and/or life-safety justifications for
performing the monitoring.– Define system-specific damage including types of
damage and expected locations.– Define the operational and environmental conditions
under which the system functions.– Define the limitations on data acquisition in the
operational environment.
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Operational Evaluation• Tests were conducted in a laboratory so operational
evaluation was not conducted in a manner that would be typically applied to an in situ structure.
• Research project so no economic/life-safety motivation.• Damage to be detected is the onset of concrete
cracking associated with yielding of the reinforcement caused by cyclic loading.
• Shaker mounting scheme was an operational constraint for these tests.
• Environmental variability was minimal.• Other testing going on in Lab at same time produced
extraneous vibration sources• Available measurement hardware and software placed
constraints on data acquisition process.
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Data Acquisition and Cleansing
15.0” (38.1 cm)
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36, 37(load)
22.67” (57.6 cm)
22.67” (57.6 cm)
22.67” (57.6 cm)
22.67” (57.6 cm)
22.67” (57.6 cm)
22.67” (57.6 cm)
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36” (91.4 cm) dia
30” (76.2 cm)
40 accelerometers mounted on the structure.
Most accelerometers were 1v/g sensitivity.
Data from low sensitivity (10mv/g) accelerometers were discarded.
Data consisted of 8-s duration time histories discretized with 8192 points (Files labeled with T)
No windowing function applied to time histories
Anti-aliasing (512 Hz cutoff frequency) and AC-coupling filters (attenuate below 2 Hz) were applied to the data
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Dynamic Excitation
• Attempted to apply a 0 - 400 Hz random signal with an APS electro-magnetic shaker at the top of the column offset from the centerline (to excited both torsion and bending response).
• Feedback from mounting plate made it difficult to obtain a uniform input over the specified frequency range
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Structural Health Monitoring Process
• The Structural Health Monitoring process includes:
1. Operational evaluation of the structure2. Data acquisition3. Feature extraction4. Statistical model development
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Data Acquisition• Obtaining accurate measures of the system’s dynamic
response is essential to structural health monitoring.• The data acquisition process can be divided into six parts:
• Signal processing is often integrated with data acquisition systems.
• Data cleansing, normalization compression and fusion is integrated in this process as well.
• The development of “system-on-a-chip” capability is allowing the data acquisition hardware to be directly integrated with thedata interrogation “software”
Data Acquisition
Excitation Sensing DataTransmission
Analogto
DigitalConversion
DataStorage
DataCleansing,
Normalization, Compressionand Fusion
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The Process of Getting a Measurement
platevibration
QuickTime™ and aAnimation decompressor
are needed to see this picture.
epoxy
viscoelasticeffects
temperature
housingself-dynamics(e.g., resonances)
boundaryconditions
seismicmass
seating
bindingpost
manufacturingtolerances
self-dynamics(e.g., cross-axissensitivity)
boundaryconditions
piezoelectricelement
coupling
stray capacitance
contact wire
contact leads
cabling
EMI
environment
signalconditioning
environmenthuman error(e.g., filter settings)
DAQboard
quantizationerror
DSP
human errorsoftware glitches(e.g., round-off)
observer
datapresentationhuman errorMicrosoft
input
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Obtaining Useful SHM Measurements I
Level 1:System-Level Considerations
What features will be extractedfrom data for SHM assessment?
Active or passive sensing?
What other factors may need to be considered for accurate assessment?
• May determine the type(s) ofprimary data to be acquired
• May determine how oftenthe primary data is collected
• Operational (e.g., loading conditionsduring primary data collection)
• Environmental (e.g., ambient conditionsduring primary data collection)
• Identification of sources of error or variability
• Determines need for activeactuation (i.e., not ambient)
• Strongly influences power,networking, and bandwidth demands
Overall imposed limitations/constraints?
• Economic (e.g.,fixed budgets)• Political (gov’t or social influences)• Environmental (e.g., regulations orrequired procedures)
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Unit-to-Unit Variability
Variability in FRFs measured on units manufactured in an identical manner with strict quality control
May need baseline measurement form each unit in this case
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Obtaining Useful SHM Measurements II
– Type of data to be acquired• Motion (e.g. acceleration)• Environmental (e.g. temperature)• Operational (e.g. traffic volume)
– Define sensor type, number and locations• Bandwidth (how fast do we sample data)• Sensitivity (dynamic range or amplitude)
– Define data acquisition/transmittal/storage system– Define how often data should be acquired
• Periodically• After extreme events
Level II: Define Data Acquisition Components.
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Obtaining Useful SHM Measurements III
– Define excitation source • Waveform (e.g. ambient, random, swept-sine)• Bandwidth (frequency range)• Amplitude
– Establish data normalization procedures• Temporal (time synchronization for multiple channels)• Level of input
– Cleanse data• Eliminate data from malfunctioning sensors• Window, decimate, filter data
– Compress data– Fuse data from multiple sources– Power for the data acquisition system
Level II: Define Data Acquisition Components (cont.).
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Putting it All Together
Data Acquisition Component Considerations
System-level Considerations
Actuator system Sensor modalities
Networking strategy
Signal conditioning module(s),synchronization, scheduling, and control
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Excitation
• Excitation is the process of applying a time-varying input to a system.
• Selection of the proper excitation methods will be influenced by many factors including:
– The size of the structure.– Required frequency range and
amplitude of inputs.– Operational constraints associated
with the system.
– Cost.
Frequency (Hz)
Electro-hydraulic
Electro-dynamic
Piezoelectric
1 10 100 1k 10k 100k
Forc
e
• Excitation methods will be classified as those where the input can be measured and those where it can not.
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Excitation Signals
• Ambient– Often the only method that can be used
for large structures, or if the structure is not to be taken out of service, or if regulations prohibit introducing energy into the structure.
• Steady-State Deterministic– (stationary periodic; chaotic)
• Nonstationary Deterministic– (impulse; step-relaxation; swept sine/chirp;
quasi-periodic)
• Random– (full random, band limited to actuation
bandwidth window; pseudo-random, constant amplitude, random phase; burst random, short in time but fully random)
Mirage F1-AZ under controlled groundvibration testing.
Ship on the high seas.
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Measured Input Excitation Examples
Tensioning cable immediately after release
Tensioning device Load cell
Electro-dynamic shaker applying random excitationto satellite (mid-frequency,fully random)
Tensioning device puttingan impulse force loadon the rotor (wide-band,transient deterministic)
Piezoelectric patches being used to impart high-frequency Lamb waves on a frame structure (periodic deterministic)
PZT patch
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Unmeasured Input Excitation Examples
0 100 200 300 400 500 600 700−1000
−500
0
500
1000
Str
ain
Signal 1
Data Point # = 26980Time Period = 601.17 secΔ t = 0.02228 sec
0 100 200 300 400 500 600 700−1000
−500
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Signal 2
0 100 200 300 400 500 600 700−1000
−500
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Time (Sec)
Str
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Signal 3
Norwegian Navy composite fast-patrol boat hull vibration strain response to wave impacts.
Di-Wan Towers (Shenzen, China) sway and vibration acceleration response to wind.
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Sensing Issues
• Number (optimal vs redundant)• Type• Location• Frequency range (bandwidth) vs frequency resolution• Amplitude sensitivity vs dynamics range• Stability (calibration requirements)• Cost (per data point, not per sensor!)• Reliability• Environmental ruggedness (durability)• Noise• Invasive (size, spark source)
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• Most modern sensors used for SHM convert measured quantity (force, acceleration, displacement, etc.) to an analog electrical signal.
• Sensors can be classified as contacting and non-contacting.
• Discrete sensors (surface point, contacting measurements) – Piezoelectric and piezoresistive force transducers– Accelerometers
• Piezoelectric• Piezoresistive• Capacitive• Servo• Fiber optic
– Strain gages• Foil• Fiber optic
– Impedance (piezo)
Primary SHM Sensing Modalities
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Non-contacting Sensors• Scanning laser Doppler velocimetry (LDV)• Laser holography• Laser/microwave displacement measurement systems• GPS (lower resolution)• Eddy Current Probe
LDV Microwave displacement
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Data Transmission
• Most modern data acquisition systems transmit the analog signal from the sensor to the A to D convert through hard-wire connections.
• Alternative is to record analog signal on magnetic tape or to record with a digital tape recorder.
• Current research is focusing on wireless data transmission of digital signals.
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Networking: Conventional Wired Network
sensor
Sensor network
Sensor network
Sensor network
Sensor networkCentralized Data Acquisition
Sensor network
Sensor network
Sensor network
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Networking: Passive Wireless Network
Base Station
De-centralized Wireless sensors
De-centralized Wireless sensors
De-centralized Wireless sensors
De-centralized Wireless sensors
•Sensor•A/D•Local computing•Telemetry•Power
Variations proposed scheme by Lynch (2003), Spencer (2004), and others
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Networking: Active, Hierarchical Wireless Sensing
Proposed scheme by LANL (G. Park) & Motorola
Base Station
•D/A, A/D
•Local computing
•Telemetry
sensor
Controlled Sensor/actuator network
Relay-based hardware
Controlled Sensor/actuator network
Controlled Sensor/actuator network
Control Node
Control Node
Controlled Sensor/actuator network
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Networking: RFID with AV Delivery
• Integrate RFid tags and UVs into SHM process for large infrastructure
Force
Conductivemetal block
Variable Capacitor
Peak Strain SensorRFID Reader RFID Tag
On the structureOn UV
InductiveCoupling
Proposed by Todd and Park (LANL), 2005
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Analog-to-Digital Conversion
• Analog to digital conversion is the process of converting the analog signal from a sensor to a digital signal that accurately represents the measured time-varying physical parameter of interest.
• Issues1. Sampling rate (maximum frequency to be resolved)2. Analog filter (prevent aliasing)3. Sampling duration (frequency resolution, Rayleighcriterion: Δf=1/T)4. Averaging (reduction of zero-mean, non-coherent noise)
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Analog to Digital Conversion
5. Quantization (converting analog signal to closest discrete value available in A to D converter hardware)– Word size (10-16 bit commonly available)– Voltage Range (amplitude range that A to D converter
can read)
Errors introduced by 3-bit A to D converter not utilizing the full voltage range
Analog signal
Discrete representation of analog signal
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Data Storage
• Record analog signals on magnetic tape• Record digital signals on magnetic tape with digital tape
recorder• Store data locally (e.g. flash ROM) with sensor and
download via communication device (hardwired or wireless)
• Store on hard disc of a computer– Store digitized data prior to signal processing– Store processed (and averaged) data after signal
processing
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References
• Excitation Methods and Signals,Sensors, Data Acquisition– McConnell,K. G., Vibration Testing Theory and Practice, John Wiley, New York,
1995.– Fraden, J., Handbook of Modern Sensors, 2nd Edition, Springer Verlag, New
York, 1996.– Dally, J. W., W. F. Riley and K. G. McConnell, Instrumentation for Engineering
Measurements, John Wiley, New York, 1992.– Doebelin, E. O., Measurement Systems : Application and Design, 4th Edition,
McGraw Hill, New York, 1990.– The Measurement, Instrumentation, and Sensors Handbook, John G. Webster
(Editor), CRC Press, 1998. – Figliola, Richard and Donald Beasley, Theory and Design for Mechanical
Measurements, 3rd Edition, John Wiley, New York, 2000– http://www.sensors-research.com
• Signal Processing– Bendat, J. and A. Piersol, Engineering Application of Correlation and Spectral
Analysis, John Wiley, New York, 1980.– Bendat, J. and A. Piersol, Random Data, John Wiley, New York, 2000.– Wirsching, P. H., T. L. Paez, K. Ortiz, Random Vibrations : Theory and Practice,
John Wiley, New York, 1995.