SE 265 Lecture 19 March 14th, 2006 - Jacobs School of ...jacobsschool.ucsd.edu/EEI/academic/courses/06/SE265/doc/SE265_19… · • Robotic and Mobile Inspection Systems. Engineering

Engineering Institute

SE 265 Lecture 19March 14th, 2006

Topics• New Studies


General Solution: Adaptive Sensor Networking

actuation


Technology Emerging to Meet Needs

• Static Arrays with Adaptive Processing– RFID-enabled sensing– adaptive local processing algorithms (information theoretics, neural

networks, automata, GA/GP)– programmable logic units (even re-programmable: current hot

research topic)

• Adaptive Vision/Imaging Systems

• Robotic and Mobile Inspection Systems


Sensor Networking with RFID Sensors• Mesh Networking (IEEE 802.15.4-based):

– Powered only when event occurs.– Event passes to neighboring RFID nodes until it is received at base

station.– Self-organizing network.– Wake-up-to-transmit feature conserves power.– Examples in use: Motorola neuRFon with WISHM (Motorola/LANL).

• Mobile Agent Wireless Sensor Network (MAWSN)– Data interrogation algorithm is passed to nodes instead of data

passed to a local processing station.– Advantages:

• Network bandwidth reduced.• More reliable than traditional wireless sensor networks.• Can support more sensor nodes.• Extensibility.


• UAV Mobile Agent:– GPS-programmed UV with onboard RFID reader and local

processing/memory.– Power provided by UV engines (several kilowatts available).– Can be controlled with a ground pilot (uses onboard cameras) or

flown on autopilot.– Data retrieval process: UV moves to sensor field location, RFID

reader transmits a read signal, data from RFID tag sensors is transmitted to reader, data interrogation is carried out onboardUV, results stored and reported to user.

Networking with RFID and UV Mobile Agents


Networking with RFID and UV Mobile Agents

sensor locations (RFID-tagged)

UV equipped with RFID reader, onboard processing, and storage

task manager node (user)


RFID Tag Technology• Components of an RFID System

– Transponder – located on the object to be monitored.

– Reader – stationary or dynamic read/write device.

• What is an RFID tag?– A Radio Frequency Identification (RFID)

tag is a transponder (microchip combined with an antenna) mounted on a substrate.

– The tag can be embedded in packaging or mounted on a device with adhesive.

– RFID tags are powered (passive tags) and read when a signal is picked up from an RFID reader. The tag returns a signal containing unique ID info and possibly sensor data.

• NOTE: RFID tags don’t have to be in the line-of-sight of the reader.

Typical RFID SystemCourtesy of: Biomedical Instrumentation & Technology (2005)

Example RFID TagsCourtesy of: www.barcode-solutions.com & www.rfidjournal.com (2005)

Microchip

Antenna coil


RFID Tag Types• Passive Tag: no power supply required

– Cost: ~ $0.40/tag.– Size: as small as 0.4 mm2 and 0.1 mm thick.– Read range: ~ a couple of mm to 5 m.Power is supplied by RFID

reader using modulation patterns:• Inductive coupling (low- and high-frequency tags)• Propagation coupling – electromagnetic capture (UHF

frequency tags)• Active Tag: requires a power source and can write more

info into a tag’s memory– Cost: ~ $10 or more per tag.– Size: as small as a typical coin.– Read range: ~ 20 -100 m.– Power source: battery, mechanical power harvesting (PZT),

solar


Comparison of RFID Tag AttributesActive RFID Passive RFID

Tag Power Source Internal to tag Energy transferred using RF from reader

Tag Battery Yes (or other power source – i.e. power harvesting or solar)

No

Availability of Power Continuous Only in field of reader

Required Signal Strength: reader to tag / tag to reader

Very low / can generate high-level signals

Very high (must power the tag) / constrained to very low levels

Read Range Up to 150 m 3 m or less

Multi-tag Reading ~ 20 tags read within 100 m of reader moving @ 100 mph

~ 20 tags read within 3 m of reader moving @ 3 mph

Sensor Capability Able to continuously monitor and record sensor input: includes date/time stamp for sensor events

Able to read and transfer sensor values only when tag is powered by reader: date/time stamp not really useful

Data Storage Up to 128 Kb (1 million bits) of read/write storage with data interrogation and search capabilities

Small storage - 128 bytes (1000 bits) of read/write storage

Cou

rtesy

of:

http

://w

ww

.rfid

exch

ange

.com


RFID Reader Frequency BandsLowFrequency High-Frequency Ultra-High

FrequencyMicrowave

Frequency Range < 135 kHz(unlicensed worldwide)

13.56 MHz(unlicensed worldwide)

860-930 MHz(US reg.: 902-918 MHz, power limits)

2.45 GHz

Read Range ~ 0.3 m ~ 1 m ~ 150 m ** ~ 50 m **

Tag Type Passive Passive Active & Passive Active & Passive

Characteristics Inexpensive, low read speed, noise

sensitive

Somewhat inexpensive, medium read range, less noise

sensitive

More expensive, high read speeds

Most expensive, highest read speeds, more of a line-of-sight

required

Example Applications

Access (keys), inventory control,

car key immobilizer, animal tracking

Access (keys), smart cards, shipment

tracking (including airline baggage), inventory control

Railroad car monitoring, large-scale

shipment tracking

Toll collection system (FasTrack, I-pass, E-

ZPass)

** Read range for passive tags in these frequency bands is much less – usually no more than 3 m.


Sensing Capability of RFID Tags

– Passive tags:• No power consumption by sensor when reader is not in range.• Energy is limited when reader is in range which limits sensor

measurements.– Active tags:

• Minimal power consumption when sensor is active.• More energy is available, but at the cost of depleting the available

“onboard” power source.

Sensor

RFID Chip• Extend RFID tag chip’s interface capability for sensors.

• Add A/D converter to existing RFID circuitry

• Sensors that could be employed:• Temperature, Moisture, Strain,

Acceleration• Incorporation of sensor on tag is easy but

sensor design is challenging.


RFID Peak-Strain Sensor for SHM• Satisfies the aforementioned sensor design

requirements.– Easily integrated into RFID chip (addition of an LC circuit).– No power required to operate.– Does not hog energy when results are transmitted.

• Advantage– $0.50/sensor - many can be deployed on a large structure.

• Disadvantage– Can’t record when peak-strain occurred.

• Peak-strain sensor operation.– Conductive metal blocks are displaced when the holding

friction force is exceeded.– Capacitance of variable capacitor is changed according to

the overlapping area of A to A*. – Peak-strain is memorized as the max capacitance change.

Peak-Strain Sensor


RFID Sensors in Use Today• Military-based

– U.S. Navy monitors temperature, humidity, and air pressure of containers storing aircraft parts.

– Active RFID tag sensor system monitors the brake temperature on F-16 planes.

• Consumer-based– Temperature-threshold-

monitoring of food products.

– Monitoring bacterial contamination of food products.

Container Temperature, Moisture, and Pressure

F-16 Brake Temperature

Food Temperature Bacterial Contamination


RFID Sensors: Recent Development• EmbedSense Wireless Tag Sensor

– Developed by Microstrain, Inc.– Bundled wireless sensor/data acquisition system.– No batteries required.– Can be used with many sensor types:

• Temperature, strain, pressure, acceleration, and load cells. – Can be used in harsh environments:

• Operating temperature up to 125°C and operating g-levels up to 50,000 g’s.

– Small design:• Outside coil diameter is 36 mm and overall thickness is 6 mm – can

be mounted on or embedded in device to be monitored.• Drawbacks

– Requires close coupling of transponder and reader: 25 – 50 mm for strain measurements from a rotating shaft.

– Cost: $3295/system (2 nodes, 1 reader).– Power: only 10 mA available for 10 msec duration pulsed bridge

excitation (passive tag sensor).


Networking Concept Advantages/Disadvantages

UCSD/LANL developingRFID sensor systems interrogated by unmanned vehicles with RFID readers:

sensor locations (RFID-tagged)

UV equipped with RFID reader, onboard processing, and storage

task manager node (user)

Advantages:• power supplied by UV mobile agent• sensing and interrogation performedautonomously

• low global bandwidth requirement• highly extensible

Disadvantages:• limited sensor capability (with currentRFID technology)

• cannot do simultaneous node inter-rogation in a network


• Initial wide area scans• Image enhancement • Bring sensor for closer examination• Human eye is very good at these effects• Human has difficulty in storage and

quantitative comparisons• Humans cannot go everywhere.

Adaptive Vision and Imaging Systems


Original unbalanced Histogram balanced

AVIS Image Enhancement


AVIS Deployment Example

Farrell St. Bridge, Burlington, VT


• Images of rebar and defects inside a concrete slab

• Image corrections required for nonlinearity arising from depth effects

AVIS Nonlinear Correction

Johannson and Mast (1994)


Robotic and Mobile Inspection Design Issues

• Safety • Mobility• Power • Control• Information storage and transfer


Laboratory Beam-Interrogating Robot

Z-world Jackrabbit control

ASM strain sensing (Microstrain)

Strain Gage Loading

0

5000

10000

15000

20000

25000

30000

0 2 4 6 8 10

Time

Stra

in B

its


Remote Powering System

• Inductively powered microtransmitter.• Magnetic near field coupling transfers power to the

receive coil. • Can accommodate virtually any electronic sensor.

Rectifier

Microprocessor

Oscillator

Sensors

Oscillator

Data logger

Demodulator

EM Power Waves(1.2 kHz)

EM Telemetry Waves

(916.5 MHz)

Antennas

Interrogation Unit Structure Under Test


Mobility Issues

Diaphragms Variable web thickness

Constrained-path robotic devices fail here without significantand expensive designs.


RC Toy Truck Autonomous Operation

Small Dent


Articulated Ultrasonic Robot Arm

LaPlatte River Bridge Girder Flange Ultrasound Thickness Measurement

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

0 5 10 15 20

Time (microseconds)

Sign

al (V

olts

)

Thin FlangeThick Flange

1.38"1.61"


UAV (Helicopter) Robotic Interrogation


MOORAD – Robot Foot

Gear driven double magnet attachment module, showing the 2 on-off magnetic surfaces (based on low-energy switching magnetic circuits)

• Rapidly deployable• Retrievable• Movable• Accelerometers• Strain gages


Biped Robot with MOORAD Feet

Climbing on steel plate Articulated camera with white LED illumination


Climbing Biped Robot


S Velinsky, B Ravani Remus Woods Hole

Hordaland

Other Robotic and Mobile Efforts


References1. http://www.rfidjournal.com/article/articleprint/1337/-1/129/.2. http://www.rfidexchange.com/DynamicPowerPoint.aspx3. www.rfidexchange.com4. www.barcode-solutions.com5. www.rfidjournal.com6. Kabachinski, J. (2005), “An Introduction to RFID,” Biomedical

Instrumentation & Technology, Vol. 39, No. 2. (www.aami.org/publications/BIT/2005/ITMA05.pdf)

7. http://en.wikipedia.org/wiki/RFID8. http://www.rfidjournal.com/article/articleview/2079. Dryver Huston, “Robotic Surveillance Approaches for SHM,”

Structural Health Monitoring 2005 (Proc. 5th IWSHM), Stanford Univ., CA, Sept. 12-14, 2005.

http://www.rfidjournal.com/article/articleprint/1337/-1/129/

http://www.rfidexchange.com/DynamicPowerPoint.aspx

http://www.rfidexchange.com/

http://www.barcode-solutions.com/

http://www.rfidjournal.com/

http://www.aami.org/publications/BIT/2005/ITMA05.pdf

http://en.wikipedia.org/wiki/RFID

http://www.rfidjournal.com/article/articleview/207


DIAMOND II SOFTWARE• Goals of the GLASS Software• Functions

– Data collection– Data cleansing and normalization– Feature extraction– Statistical discrimination

• GLASS Software– Development Software– Node Software

• Husky Node Hardware– Hardware System Comparison– Remote Connectivity

• Demonstration of the GLASS software• Demonstration of the Husky node• Wave Propagation Toolbox for Active Sensing


Goals

• Goals– Catalog & encapsulate

functions– Develop client software– Develop node software– Demonstrate a working

system

• Accomplished– Cataloged over 30

functions– Created GLASS client

software– Created GLASS node

software– Demonstrated monitoring

of a simple structure


Functions• Statistical Pattern Recognition Paradigm

– Data Collection• Read in from data acquisition files• Read in from MATLAB• Collect from integrated sensor hardware

– Data Cleansing and Normalization• Filtering• Statistical normalization• Neural Networks

– Feature Extraction• AR-ARX• ARMA• Impedance• Holder Exponent

– Statistical Discrimination• Extreme Value Statistics, Control Charts• Sequential Probability Ratio Test


Statistical Discrimination

• Extreme Value Statistics– Gumbel– Weibull– Frechet

• Control Charts– X-bar chart– S-bar chart


• Distill a catalog of reusable functions.• Connect functions through a standard data structure.• Create an interface to graphically assemble individual

functions into processes.• Allow functions to be connected even when written in

dissimilar languages (MATLAB, JAVA, C).• Typical software functionality of saving, loading, and

editing processes.• Link the graphical client to a node where processes are

run on remote hardware.

GLASS Client SoftwareGraphical Linking and Assembly of Syntax Structure


GLASS Client SoftwareGraphical Linking and Assembly of Syntax Structure

CategoriesProcess

Workspace

Functions

Modules


GLASS Node Software

• Dynamic embedding of a process created with the GLASS client into a hardware environment.

• Continuous running of the embedded process.• Instant process restart capabilities after a reboot.• Internet enabled access and control from a

GLASS client.


Husky Node Hardware

• PC-104 Standard– Single Board Comp

• Pentium 233 MHz• 256 Mb RAM• 512 Mb CF card• Linux OS

– Motorola DSP Board• 6 Analog Inputs• 4 Analog Outputs

– Motorola Wireless• 802.15.4• 10 m range• Self organizing net


Hardware System Comparison


Remote Connectivity


Integrating Hardware and Software for Active-Sensing based SHM


Health Of Plate Structures (H.O.P.S. module) was developed to merge the various analysis methods of active sensing

Lamb: Wavelet Impedance

Reflection: Triangulation

Time Reversal Acoustics

Dat

a A

cqui

sitio

n Se

tting

sScot Hart (stanford),

Eric Flynn (Cal tech.),

Andrew Swartz (Michigan),

Dan Backman (Ohio State)


Automatically define geometry & sensor/actuator pathes


Data Acquisition parameters can be easily set and controlled

• Can be saved and dynamically loaded.

• Hardware will include Smart suite cases, NI systems, Motorola hardware, or generic DAQ cards


Surface Effect Fast Patrol Boat

(a) Surface-Effect Fast Patrol Boat

A,B

F

J

E,F

EG

G

D

B

A

C

C,DK

H

I

H,I

K J

(b) Fiber optic strain gauges


Raw Time Series Data

0 100 200 300 400 500 600 700−1000

−500

0

500

1000S

trai

n

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


Testing Procedure

Signal 1 Training Testing


Signal 3 Testing Testing




The first 40 Random Tests




Signal 1 Testing

Signal 2 Testing Training


Training

The next 40 Random Tests


A Look-up Table Technique

∑=

−=p

jyjxjtx 1

2

)()(min φφ

Find the segment x(t) closest to y(t)

such that

)()()(1

tejtxtx x

p

jxj +−= ∑

=

φ )()()(1

tejtyty y

p

jyj +−= ∑

=

φ

x(t) y(t)

Fit AR model


Construct AR-ARX Prediction Model

)()()(1

tejtxtx x

p

jxj +−= ∑

=

φ )()()(1

tejtyty y

p

jyj +−= ∑

=

φ

1. Fit AR models to x(t) and y(t)

4. Define as our damage-sensitive feature )2(/)2( xy εσεσ

2. Fit an ARX model to x(t) and pair and estimate)(tex )(txεba

∑∑==

−−−−=j

xji

ix jteitxtxt11

)()()()( βαε

3. Estimate using )(tyε ji βα and

∑∑==

−−−−=b

jyj

a

iiy jteitytyt

11

)()()()( βαε


Damage Identification

α

εσεσ

1,12

2

)()(

−−>xy nn

x

y F

* Reject the null hypothesis if

* Box and Andersen (1955) generalize the F-test to non-normal distribution.

)()(: 220 yxH εσεσ =

)()(: 221 yxH εσεσ <

* Hypothesis Test


• For record length of 1148 pts an AR(30) model will yield 1118 residual error estimates

• Now if we fit ARX (5,5) model, using residual errors of AR model as input quantities, we will get 1113 estimates of ε.

• Use stmbc command in Matlab to estimate a and b values of the ARX model.

• When using a and b values to predict time series note that b values include a term that multiplies the current input values as well as the five previous input values.

• The a values obtained with LPC will have six terms, but the first value is one so they are estimating the current output based on 5 previous outputs

• Using the flipud command to reorder a and b vectors to help calculate the estimate of the measured time series form the AR or ARX models.

Documents

SE 265 Lecture 19 March 14th, 2006 - Jacobs School of ...jacobsschool.ucsd.edu/EEI/academic/courses/06/SE265/doc/SE265_19… · • Robotic and Mobile Inspection Systems. Engineering