se 265 lecture 3 january 17, 2006 topics 1. review uc...

Engineering Education Initiative

SE 265 Lecture 3January 17, 2006

Topics1. Review UC-Irvine Bridge Column Test2. Data Acquisition


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


Example: UC-Irvine Bridge Pier


Cyclic Loading


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.


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.


Data Acquisition and Cleansing

15.0” (38.1 cm)

34

35

40

32

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39

30

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38

28

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2

26

27

1

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22

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)

X

Z

3

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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


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


Test Configuration


Structural Health Monitoring Process

• The Structural Health Monitoring process includes:

1. Operational evaluation of the structure2. Data acquisition3. Feature extraction4. Statistical model development


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


Piezoelectric Accelerometer

Courtesy of PCB, Inc.


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


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)


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


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.


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.).


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


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.


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.


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


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

0

500

1000

Str

ain

Signal 2

0 100 200 300 400 500 600 700−1000

−500

0

500

1000

Time (Sec)

Str

ain

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.


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)


• 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


Non-contacting Sensors• Scanning laser Doppler velocimetry (LDV)• Laser holography• Laser/microwave displacement measurement systems• GPS (lower resolution)• Eddy Current Probe

LDV Microwave displacement


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.


Networking: Conventional Wired Network

sensor

Sensor network

Sensor network

Sensor network

Sensor networkCentralized Data Acquisition

Sensor network

Sensor network

Sensor network


Networking: Passive Wireless Network

Base Station

De-centralized Wireless sensors




•Sensor•A/D•Local computing•Telemetry•Power

Variations proposed scheme by Lynch (2003), Spencer (2004), and others


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



Control Node

Control Node



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


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)


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


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


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.

se 265 lecture 3 january 17, 2006 topics 1. review uc...

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