Post on 15-Mar-2020
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SE 207 Lecture May 23, 2006
Topics1. Structural Dynamics Measurements
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All SD Sensor Networks are Deployed for Three Possible Uses
1. Detection Problems (existence and location)
– Threshold detection (air bag deployment)– Structural Health Monitoring
2. Model Development, Validation, Uncertainty Quantification
– Finite Element Model Validation– Damage Prognosis (predicting remaining system
life)3. Control Systems
– Avionics– Manufacturing Processes
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Sensing Systems for Damage Prognosis
Initial System Information: Design, Test, Maintenance,
Operator “Feel”
Estimate: Remaining Service Life
Time to FailureTime to Maintenance
Data Based
Initial Physics Model
Future Loading Model
Structural Health Model
Prognosis Model
Operational and Environmental Measurement
system
Structural Health Monitoring
Measurement System
Updated Physics Model
Physics Based
Take action, update system information, continue process
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The Structural Health Monitoring Process
(or any other detection problem)
1. Operational evaluationDefines the damage to be detected and begins to answer questions regarding implementation issues for a structural health monitoring system.
2. Data acquisitionDefines the sensing hardware and the data to be used in the feature extraction process.
3. Feature extractionThe process of identifying damage-related information from measured data.
4. Statistical model development for feature discriminationClassifies feature distributions into damaged or undamaged category.
• Data Cleansing• Data Normalization• Data Fusion• Data Compression(implemented by
software and/or hardware)
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Definition of “Damage”
• Damage is defined as changes to the material and/or geometric properties of a structural that adversely affect its performance.
• All materials used in engineering systems have some inherent initial flaws.
• Under environmental and operational loading flaws will grow and coalesce to produce component level failure.
• Further loading causes system-level failure.
• Must consider the length and time scales associated with damage evolution.
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Humans are Enamored by Things of Extreme Size
Radio Telescope MEMS Sensors
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Example: UC-Irvine Bridge Pier
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Cyclic Loading
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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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Data Acquisition• Obtaining accurate measures of the system’s dynamic
response is essential to model validation.• 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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Piezoelectric Accelerometer
Courtesy of PCB, Inc.
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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 SD Measurements I
Level 1:System-Level Considerations
What features will be extractedfrom data for SD application?
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 SD 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 SD 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
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Signal 1
Data Point # = 26980Time Period = 601.17 secΔ t = 0.02228 sec
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Signal 2
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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 SD 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 SD 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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Traditional SD Sensing System
sensor
Sensor network
Sensor network
Sensor network
Sensor networkCentralized Data Acquisition
Sensor network
Sensor network
Sensor network
Drawbacks1. Hard to
deploy in retrofit mode
2. Need AC power
3. One-point failure sensitive
4. Wires can be expensive to maintain
AdvantageOff-the-shelf systems readily available
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New SD Sensing Systems Paradigms
Sensor Node•Sensor•A/D•Local computing•Telemetry•Power (battery)
Base Station
De-centralized Wireless sensors
De-centralized Wireless sensors
De-centralized Wireless sensors
De-centralized Wireless sensors
Employs reconfigurablehopping protocol to telemeter data to base station
Drawbacks:1.From powerconsumption standpoint, node nearest the base station tend to do more work2. Lot of redundant hardware3.Limited processing
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Wireless Sensing Unit Prototype
Completed Wireless Unit
Performance SpecificationCost $100 per unit
Form Factor 10 cm x 6 cm x 2 cm
Energy Source 5 AA Batteries
Power 80 mA @ 5V
Data Rate 38.4 Kbps
Range 100 - 300 m
Sample Rate 100 kHz
Printed Circuit Board with Components Surface Mounted
8-bit Microcontroller128 kB SRAM
4-channel A/D Converter
Sensor Channels
Jerome P. LynchDepartment of Civil and Environmental EngineeringUniversity of Michigan, Ann Arbor MI
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Relay-based multiplexing hardware
Drawbacks
Active, Hierarchal Wireless Sensor Paradigm
:1. Nodes nearest the base station still do more work.2. More power needed at a particular node.
Let’s eliminate some of the redundant hardware
sensoractuator
energy harvesting
Proposed scheme by LANL (G. Park) & Motorola
Base StationControlled Sensor/actuator network
Controlled Sensor/actuator network
Controlled Sensor/actuator network
Control Node
Control Node
Controlled Sensor/actuator network
•D/A, A/D
•Local computing
•Telemetry
•Rechargeable battery
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AccelerometerFBGPZT
strain gage
Interface unitADC, DAC, DSP
6 sensor input4 actuator output
MicrocontrollerSingle Board
ComputerRunning LINUX
MC13192Zigbee
Host Node&
Server(laptop)
Sensor Module Computation Module User InterfaceCommunications
Module
DIAMOND II Software
Healthy?Damaged?
Motorola/LANL SHM System
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The Future: Integrating Global and Local Sensing Systems
Examples:– Lamb wave propagation– Impedance method– Time reversal analysis
Examples– Modal testing & analysis– System identification– Operational and Environmental
monitoring
Active Local System – High frequency response– Local damage identification– Active sensing
Passive Global System Response– Low frequency response– Global vibration based monitoring– Passive sensing
1st Torsion
Airplane SpeedR
eson
ance
Fre
quen
cy
1st Bending
Detect local delamination using NDE techniques
Skin delamination can lead to drop in torsion frequency
Flutter speed
The merge of torsion and bending modes leads to FLUTTER!!
(G. Park and H. Sohn)
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LANL/Honeywell High Explosives Radio Telemetry System
HERT Mark II with 10 watt power amplifier64 optical inputs10 ns resolutionApproximately 2.2 Kg Field Programmable Gate Array for onboard data processing
Flight hardened!!100 Mbit/s data transmission
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Powering Active Sensor Node with RF Energy
Piezoelectric sensor
ADG 408 Multiplexer
(up to 8 sensors)
AD5934 Impedance
Chip
Xbee Radio
ATmega128L
Voltage Multiplication and
Energy Storage
Xbee Base2.4 GHz RF data link
X-band Source
Power delivered to sensor node by X-band radar source
Impedance measurement
chip
Microcontroller/ Telemetry/Energy
Base Station
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Networking: RFID with AV Delivery
• Integrate RFid tags and UVs into SHM process for large infrastructure
Force
Conductivemetal block
RFID Reader
Variable Capacitor
Peak Strain SensorRFID Tag
On the structureOn UV
InductiveCoupling
Proposed by Todd and Park (LANL), 2005
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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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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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Aliasing
• Aliasing is the inability to accurately resolve higher frequency components of a signal when sampling at a specific rate.
• Aliasing is a result of sampling data at a finite rate.
• Requirement: Fsamp > 2.0 x Fmax (Shannon’s Theorem)
• Aliasing is controlled with both analog and digital filters.
• Most modern data acquisitions systems use a single analog anti-aliasing filter that is based on the maximum sampling rate of the system.
• Digital filters are then used for the smaller frequency bandwidths of interest.
• Must account for roll-off in filters.
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Aliasing: Time Domain
Sampling too slowly gives a false indication of the signal waveform.
Actual Sinusoidal Waveform
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Time (sec)
Mag
nitu
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DigitizedWaveform
h ≥ 1/(2*fmax)
h=20*fmax
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Folding and the Nyquist Criterion
f
Mag
nitu
de
fN
frequencyNyquist /2
frequency sample
==
=
sN
s
ff
f
• All frequencies not in the range 0 ≤ f ≤ 1/(2*h) will be undersampledand therefore manifest themselves as lower frequency components (alisaing).
• Frequencies fh > fN will be indistinguishable from their alias:
fh=Δf + fN looks like fl= fN - Δf
• The Nyquist frequency is also called the folding frequency since aliased frequencies have principal aliases as if the f’ > fN were folded back into the range < fN
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Leakage
• Leakage refers to errors caused by deviations from the assumption that the signal is periodic within the sample period.
• Leakage errors manifest themselves in the form of “smearing” the frequency components of the “true” Fourier transform to other neighboring frequencies. These errors tends to widen and distort the Fourier transform.
• Leakage is one of the most common sources of bias error in signal processing.
• Leakage can be controlled by experimental and sampling procedures such as averaging, increasing the frequency resolution, and using periodic excitation.
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Leakage
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TIME0 5 10 15 20 25 300
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FFT
t=[0:999]/1000;x=sin(2*pi*2*t)xf=fft(x);plot(abs(xf(1:30))
t=[0:999]/1000;x=sin(2*pi*2.2*t)xf=fft(x);plot(abs(xf(1:30))
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If the period fits the time, the spectrum is correct
If the period does not fit the time, spurious spectral lines result
TIME
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More Leakage
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Plot(x)
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Plot([x,x,x])
FT is assuming
FT is assuming
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Windowing
• One of the most common methods used to reduce leakage effects (it can not be eliminated) after the data has been acquired is to window the data.
• Windowing is applied to a signal by multiplying it by an appropriate function. This multiplication forces the data to begin and end at the same time, thus preventing a discontinuity in the data when repeated.
10 20 30 40 50 600
0.5
1Hanning Window
w = hanning(N);⎥⎦
⎤⎢⎣
⎡⎟⎠⎞
⎜⎝⎛
−−=+
12cos15.0]1[
nkkw π
k=0,1,…n-1
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0 2 4 6 8 100
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z)Fr
eque
ncy
(hz)
Freq
uenc
y (h
z)
t1 = [1:2000]*h; f1 = [0:1999]/2000/h;sig1 = sin(2*pi*t1); sigf1 = abs(fft(sig1));plot(f1,sigf1);
f2 = [0:3999]/4000/h; sig2 = [sig1 zeros(1,2000)];sigf2 = abs(fft(sig2)); plot(f2,sigf2)
sig3 = sig2.*hanning(4000)';sigf3 = abs(fft(sig3));plot(f2,sigf3);
FFT of a sine signal whose periodicity aligns well with the length of the signal.
Zero-padding the signal produces a discontinuity which causes side lobes.
Applying a Hanningwindow to the data increases the width of the main lobe, but eliminates most of the side lobes.
Windowing Example
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Windowing Reduces Leakage
• Hanning window reduces leakage, but broadens effective bandwidth.
• Also increases distortion and reduces signal to noise ratio.
0 0.2 0.4 0.6 0.8 1-1
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Time (sec)
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Time (sec)
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F (h )
log(
Mag
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No Window
Window
Autospectrum
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Coherence
)()()(* fXfHfYxhy =⇒=
)()(
)()(
)()()()()()()()(
)()()()(
2
2
fHfH
fHfX
fHfXfXfHfXfXfHfX
fYfXfYfX
===⋅
=γ
• The magnitude squared of the coherence function is then:
• The magnitude-squared coherence is a real function between 0 and 1.
• It provides a measure of the noise present in the system.
• Let’s assume that Y is produced from X via a linear-time invariant process:
1)()(2 ==
fHfHγ
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Coherence
• A common use for the coherence function is in the validation of input/output data collected in a vibration experiment for purposes of system identification.
• For example, X(t) might be a known signal which is input to an unknown system and Y(t) is the recorded response. Ideally, the coherence should be 1 at all frequencies. However, this is not always true.
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Coherence
• Coherence can be used to measure the degree to which the response Y is a linear function of the Input X.
( )( ) ( )
( ) ( )∑ ∑∑ ⎟⎟
⎠
⎞⎜⎜⎝
⎛
=blocks blocks
ii
blocksii
fXfY
fXfYf 22
2
2γ
( )( )
( ) ( )∑ ∑∑ ⎟⎟
⎠
⎞⎜⎜⎝
⎛
=blocks blocks
ii
blocks
fXfX
fXf
i
222
22
2
α
αγ
( )( )
( ) ( )∑ ∑∑ ⎟⎟
⎠
⎞⎜⎜⎝
⎛
=blocks blocks
ii
blocks
fXfX
fXf
i
222
222
2
α
αγ
( )( ) ( )
( ) ( )∑ ∑∑∑
=blocks blocks
ii
blocksblocks
fXfX
fXfXf
ii
22
22
2γ
( ) 0.12 =fγ (indicates linear relationship)
Let Y(f) =αX(f)
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Why is Coherence Sometimes Low?
1. Fundamentally, response is not a linear function of the input. The system is nonlinear.
2. Noise uncorrelated to input contaminates the system response.
3. Leakage between blocks (explained next).
4. Inadequate frequency resolution.
5. Poor signal to noise ratio
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Poor SNR and Frequency Resolution
If you remove the noise from the system, the coherence drops only at the resonances, indicating inadequate frequency resolution.
Drops at ends due to noise outside the passband.
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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.