A u gu st 3,31,
2005
January
2006
1
ICE is High-Tech
• High-Tech Manufacturing:
– Measuring and Control Instruments
- Instrumentation
- Controls
– Computers & Peripheral Equipment
– Communications Equipment
– Consumer Electronics
– Electronic Components and Access
– Semiconductors
– Defense Electronics
– Photonics
– Electromedical Equipment
ICE
• Communications Services: Wired, Wireless, Satellite
• Software and Tech Services: Software Publishers,
System Design, Internet, Engineering
Computer
3
ICE is Membership
4
Target Market
Primary Customers (at the present time):
• Industry – existing members:
– ABB
– Rockwell
– Keithley
– Orbital
• Industry – new members
• NASA
– Moon, Mars
• Test bed
• Manufacturing in space
5
Academic Involvement
• Primary Partners
– Case, Akron, CSU
• Secondary Partners
– NASA, OSU, Kent State
• Developing/Expected Partners
– University of Dayton, University of Cincinnati,
Youngstown State, Toledo, Zane State, Stark
State, Cleveland Institute of Art
Mark Tumeo
6
Research, Products, and Services
• Pieces Already In Place
– “Translational Research” fund in place:
• Initially funded by Federal grant and private donors
• Board of Directors led by business
– Venture Capital access:
• Through Ohio Innovation Fund provide direction and
guidance on accessing venture funds
• Through Jumpstart, Inc. provide professional review,
support and potential funding for most promising
Start-ups
7
Research, Products, and Services
• Pieces Already In Place
– Pre-arranged Intellectual Property Agreements
for Ohio ICE Members:
• Sets mutually accepted terms on ownership, licensing
and royalty arrangements for ALL types of research
funding
• Eliminates uncertainty and reduces “administrative”
delays for research contracts
– Network of higher education institutions across
Ohio:
• Provides access right at industry’s “back door”
• Leverages the 3rd Frontier “Dark Fiber” Network to
provide access statewide
8
ICE is research
• Industry-University Consortium
– Integration of computing, communication, measurement, and
control
– Align the technology needs of industry with the multifunction
needs of academia
– Increase research support for electrical engineering and
computer sciences
• Research
– Perform industrially relevant research that improves
industrial capacity, production and efficiency
– Perform research that develops new concepts, processing
methods, and new analytical techniques
10
Research Products & Services
Research is 100% industry driven!
• Technical Advisory Committee (TAC):
– Representation from industry and academia
– Confirm focus of the research is in alignment with needs of ABB,
Keithley, Rockwell, and other Industrial Partners
– Review, refine, approve proposals submitted by associated
Universities
– Process tested over the last six months: Case/Akron proposal
• Industry Benefits:
– New talent trained in fields of instrumentation, controls, and
electronics
– Help advance state-of-the-art and provide new employees with
these state-of-the-art skills.
– Neutral workshop with competitors where can work on compatibility
between products and develop industry standards
11
Research Needs
• Sensor issues
• Advanced Motion Control issues
• Networked, Distributed Control issues
• Hard to separate these three areas as each
impacts the bigger issues that companies
such as ABB, Rockwell, etc. are trying to
solve
12
Networked Control
13
Networked Control
• Computing in the
physical world
• Components
– Sensors, actuators
– Controllers
– Networks
• Enables
– Operations in hazardous
environments
– Timely remote support
– Continuous operations
• Remote monitoring
• Troubleshooting
– Reduce time, effort, cost to
develop and upgrade
applications
• Merge cyber- and physicalworlds
14
Example
• Physical environment
– Pipes, levers
– Switches
• Sample task
– Close lever
• Robot
– Actuators
• Arm, gripper
– Sensing
• Force feedback
• Visual feedback
– Control
• Local compliant control
• Remote supervision
(Joint work with W. Newman, A. Al-Hammouri)
15
Hardware
Security
Networked Control
Software
Engineering
Diagnostics
16
Hardware
Leaders in Instrumentation, Controls & Electronics
Partners in Economic Growth
Wireless Sensor Platform for
Harsh Environments
Prof. Steven L. Garverick
X. Yu, L. Toygur, Y. He, M. Crane
Wireless Sensor Platform
Objectives and Applications
• Objectives
– Low-power and robust, wireless microsensors
• Unobtrusive sensing
• Harsh operating conditions
– High temperature
– Mechanically/chemically active environments
• Applications
– Automotive, aerospace, and geothermal industries
– In-vivo tissue and blood sensing for health monitoring and
treatment
– In-situ monitoring of liquids and gasses for contamination
control and security
18
Wireless Sensor Platform
Approach
Vdd
SOI IC
1 MHz
Vin+
+
R+R
R+R
VS+
Rm Amp
R
R
Vss
15 kHz
8-bit 1stst-order
-order
8bit,1

ƒ°-ADC
Ģ
1bit
Vin--
125 kbps
nd-order
22nd
-order
Decimation
P/S
Decimation
8bits
Filter
Filter
VCO
Preamplifier
VS-
Signal Magnitude
Sensor
fmax:7.5KHz
o
ati
m
i
c
De
N
fd/2
7.5KHz
s
oi
r
fte
a
e
er
ilt
f
n
start
Quantization Noise
data
stop
FSK
n
io
at
m
ci
e
d
fs/2
500KHz
19
SOI Test IC
Die Microphotograph
Decimator
VCO
Sigma-Delta
Modulator
Bias
Rm Amp
Test Structure
20
SOI  ADC
DC Tests at Room Temperature
Nominal operating conditions
DC transfer characteristics at room temperature
1 MHz
Bandwidth
8 kHz
Input Amplitude
3 Vp-p
Input Frequency
3 kHz
OSR
64
Performance summary
Power
Consumption
200 w
Max. Input Level
3 Vp-p
DC Transfer Characteristic
1200
Data
Linear fit
1000
Number of 1s in 1000 samples
Sampling
Frequency
800
600
400
200
0
Dynamic Range
40 dB
-200
SNRMAX
55 dB
-1.8
-1.4
-1
-0.6
-0.2
0.2
0.6
Differential Input Amplitude (V)
1
1.4
1.8
21
SOI  ADC
AC Tests at Room Temperature
FFT magnitude of the  output
SNR vs. input amplitude
60
80
@ nominal conditions
Number of points = 16384
The 16, 48, 80 .. kHz dither
70
60
55
50
SNR (dB) Average Value:(*)
Amplitude (dB)
50
40
30
20
10
0
45
40
35
30
25
-10
20
-20
2
4
6
8
Frequency (Hz)
10
12
14
4
15
-40
-35
-30
-25
-20
-15
Input Voltage (dBFS)
-10
-5
0
5
x 10
22
SOI  ADC
High Temperature Test
SNR versus temperature
60
High-temperature test setup
50
Instruments
..
.
.
Thermal grease
SNR (dB)
Connector
Tube
40
Hot Plate
30
20
10
Thermocouple
DIP
@ nominal conditions
0
-10
27
50
100
150
200
Temperature (°C)
250
300
23
SOI Rm Amplifier
Test Setup
Measurement setup for Rm amplifier
Ceramic-on-gold Module
SOI IC
Vout
Rin
Cin
Vin
Rm Amplifier
CL
Resistor
Tunnel diode
SOI IC
• DIP package
– Pin coupling > 15 fF caused
oscillation at ~1 MHz
Capacitor
• Gold-on-ceramic module using
bare die
– Oscillations continue
– With CL = 100 pF, oscillations stop
and BW  700 kHz
24
SOI Rm Amplifier
High Temperature Test Results
response for different
temp
MagnitudeAC
response
vs. frequency
1.00E+08
~500 kHz
Rm = ~ 8 MW
Gain(ohms)
1.00E+07
Gain(25 °C)
Gain(50 °C)
Gain(100 °C)
Gain(150 °C)
Gain(200 °C)
Gain(250 °C)
Gain(270 °C)
Gain(300 °C)
1.00E+06
1.00E+05
1.00E+04
1.00E+02
1.00E+03
1.00E+04
1.00E+05
1.00E+06
1.00E+07
Frequency(Hz)
25
SOI Rm Amplifier
High Temperature Test Summary
Passband gain vs. Temperature
Passband bandwidth vs. Temperature
Passband Gain v.s. Temperature
Passband bandwidth v.s. Temperature
9.00E+06
1.40E+06
8.00E+06
1.20E+06
Passband bandwidth (Hz)
Passband Gain (Ohms)
7.00E+06
6.00E+06
5.00E+06
4.00E+06
3.00E+06
1.00E+06
8.00E+05
6.00E+05
4.00E+05
2.00E+06
2.00E+05
1.00E+06
0.00E+00
0.00E+00
0
50
100
150
Tem perature(°C)
•
•
•
200
250
300
0
50
100
150
200
250
300
Temperature(°C)
The frequency response for temperatures up to 250 C is nearly ideal:
Rm = 8.3 MegW, fL = 1 kHz, fH = 500 kHz
The transimpedance gain decreases at temperatures above 250 C
The amplifier continues to function well at temperatures up to 300 C
26
Leaders in Instrumentation, Controls & Electronics
Partners in Economic Growth
Diagnostics
Diagnostics and Prognostics:
Sensor and Algorithm for Health
Monitoring in Industrial Systems
Kenneth A. Loparo
Motor and Gearbox
Diagnostics and Prognostics
Motor Diagnostics:
-rotor unbalance
-rotor bar faults
-stator winding faults
Gear Diagnostics
Motor and Gearbox
Health Monitoring
System
Lube Diagnostics
Bearing Diagnostics
28
Lubricant Health Monitoring: Signal
Processing, Diagnostics and
Prognostics
MEMS Sensor
Feature Extraction
Sensor
n
Decision
Level fusion
Water
contamination
Indicator
1(1)
preprocessing
preprocessing
Lubricant Failure
Space
Feature vectors
Temperature
TAN
ElectroChemical
Conductivity
Sensor
1
Data Level
Fusion
Estimation
of
Lubricant
Health
Indicators
Machine
Health
Assessment
Indicator
m(1)
overheating
Data
Association
Indicator
1(n)
Decision
fusion
Machine
Health
Prediction
Lubricant Health
Estimation
History
Indicator
m(n)
History
Remaining
Useful life
Estimation
History
History
Lubricant information
29
Experimental Results (Prognosis)
HMM Probabilities given HMM
for Normal Condition
SKF6204 Bearings
• Failed in 50 days
• Speed = 10012 rpm
• Load = 340 lbs (axial)
• T = 260oF
• Fs = 24 kHz
0
-1000
Log Probabilities
Log Probability
-2000
-3000
-4000
-5000
-6000
-7000
-8000
0
5
10
15
20
25
Day
30
35
40
45
50
42
Leaders in Instrumentation, Controls & Electronics
Partners in Economic Growth
Networked Control
Networked Control Systems
Michael S. Branicky
Networked Control Systems
• Numerous distributed agents
• Physical and informational dependencies
•Control loops closed over heterogeneous networks
44
Fundamental Issues
• Time-Varying Transmission Period
• Network Schedulability
• Network-Induced Delays
• Packet Loss
Plant
Plant
Controller
Delay
Delay
Controller
Controller
.
.
.
Plant
h1(t)
Network
h(t)
h
hN(t)
Plant
Plant
Controller
r
Controller
[Branicky, Phillips, Zhang: ACC’00, CSM’01, CDC’02]
45
Control and Scheduling CoDesign
• Transmission scheduling
satisfying network bandwidth
constraints
h1(t)
Plant
Controller
.
.
.
Network
• Control-theoretic
characterization of stability and
performance (bounds on
transmission rate)
hN(t)
Plant
Controller
Simultaneous optimization of
both of these = Co-Design
[Branicky, Phillips, Zhang: CDC’02]
46
Co-Simulation Methodology
[Branicky, Liberatore, Phillips: ACC’03]
Packet queueing
and forwarding
Controller
agent
(SBC, PLC, …)
Network dynamics
Visualization
Plant agent
(actuator,
sensor, …)
Router
Bandwidth
monitoring
Plant output
dynamics
Simulation
Co-simulation of systems and networks languages
47
Co-Simulation Components (1):
Network Topology, Parameters
ns-2 package used to simulate network at packet level:
• state-of-art, open-source software
• follows packets over links
• queuing and de-queuing at router buffers
• GUI depicts packet flows
• can capture delays, drop rates, inter-arrival times
Our simulations (heterogeneous links, diff. queue sizes):
• Fast Ethernet links, switches, 48B packets
• T1 line with 1.544 MB/s (from router to controller)
• FTP cross-traffic: TCP SACK/DelAck, Internet params.
48
Co-Simulation Components (2):
Plant and Controller Dynamics
Extension of ns-2 release (written by Liberatore):
• plant “agents”: sample/send output at specific intervals
• control “agents”: generate/send control back to plant
• dynamics solved numerically using Ode utility,
“in-line” (e.g., Euler), or through calls to Matlab
Our simulations (scalar, NL inv. pendulum, aircraft):
• identical unstable plants, sensors sampling periodically
• controller stabilizes plant, which is event-based
• actuators receive/exert control and are event-based
• one (distinguished) plant
49
Analysis and Design Tools
• Stability Regions [Zhang, EECS, Ph.D., May 2001]
• Traffic Locus
[Hartman, EECS, M.S., Jun. 2004]
Both for an inverted pendulum on a cart (4-d), with feedback matrix
designed for nominal delay of 50ms. Queue size = 25 (left), 120 (right)
50
Summary
• Reviewed Networked Control Systems (NCS)
• Summarized Fundamental Issues, Co-Design
• Introduced a Co-Simulation Methodology, Code
• Presented Analytical/Design Tools:
Scaling, Heterogeneity (links, traffic)
51
Leaders in Instrumentation, Controls & Electronics
Partners in Economic Growth
Software
Engineering
Software Engineering:
Middleware and Agents
Vincenzo Liberatore
Middleware
• Dealing with complex systems
• Explicit structure allows
identification, relationship of
complex system’s pieces
– Layered reference model for
discussion
• Modularization eases
maintenance, updating of
system
– Change of implementation of
layer’s service transparent to
rest of system
– E.g., change in data link doesn’t
affect rest of system
Application
(the control application, e.g., close-lever)
Middleware
(common to multiple applications,
e.g., resource discovery)
Transport
(e.g., TCP, RTP/UDP)
Network
(convergence layer: IP)
Data Link
(low level communication,
e.g. Ethernet, Infinet, etc.)
53
Resource Discovery
• Plug-and-play
– Add new resources on
the fly
– Example: USB
• Plug in a USB camera
on a USB port
• But now we want: on a
network, with arbitrary
units
• Example
– Locate a robot on the
network
54
Jini
• Operations
– Discover, Join, Look-up, Use
• Programming
– Include a library
– Use functions
• Fault-tolerance
– Leases
• Join only last for a certain
time period
• Renew the lease
– Multiple look-up servers
– JavaSpaces
• Distributed shared memory
• URL: www.jini.org
Courtesy of Sun Microsystems
55
Middleware
•
Between application and transport
•
Applications
•
Software development
•
Middleware over IP
– Libraries to provide advanced functionality
– Hide communication
–
–
–
–
–
–
Resource Discovery
Remote Procedure Calls
Security
Interoperability (e.g., since Real-Time Corba)
Scheduling, resource management, performance analysis
Multicast
– Simpler, faster
– State-of-the-art functionality
– Wealth of libraries for IP
– Critical advantage of the Internet Protocol
56
Agents: Objectives
• Survivability and fault-tolerance
• Safety and security
• Cope with unstructured physical
environments
• Unified protocols across human-robotic
networks
• Software re-use
57
Agent: Objectives (contd)
• Tolerate low network Quality-of-Service
– Long-haul delays, packet losses
• Unit aggregation and cooperation
• Evolvability
– Re-programmability
– Dynamic reconfiguration
– Extensibility
58
Vision: Agent-based
• Basic properties
–
–
–
–
–
Autonomous, mobile
Adaptable, flexible, reactive
Knowledgeable, goal-oriented, learning
Collaborative
Persistent
• Agents for robots
– Aggregation into task-oriented teams
– Evolvable
• Re-programmability, reconfiguration, extensibility
59
Agent types
Virtual Robots: The Core
GUI, interface
Thin-legacy layer
On-board controllers
60
Hierarchical organization
Chain of command
61
Example
Open/Close 

Agent-based
software
MoveTo


RPCS
Virtual Supervisor
62
Security
Leaders in Instrumentation, Controls & Electronics
Partners in Economic Growth
Security:
Post-deployment Validation
Andy Podgurski
Vulnerabilities
• Vulnerabilities
– Possible origin: software defect
– Present after deployment
• Must identify latent defects early
• AAA
– Authentication, Authorization, Accounting
– Defect and vulnerabilities
• E.g., OpenLDAP ITS 1530 “Anonymous user can use
ldapmodify to delete user attributes”
64
Mining Profiles
• Profiles
– E.g., count of function calls
– Previous objectives
• Compiler optimization
• Detect defect
– Detect related vulnerabilities
• Mining and Audit
– Mine and visualize profiles
– Drives manual audit
• Avoid false positives
– Objectives
• Detect unusual executions
– Unusual executions known to be positively correlated with defects
• Cluster similar executions
65
Example
• Methodology
– Synthetically generate
executions
– Profile OpenLDAP
function calls
– Multidimensional scaling
to produce 2D display
• Observations
– Troublesome executions
in an identifiable cluster
Anonymous user deletes
attributes
66
67
A u gu st 3,
2005
January
31,
2006
68
Descargar

Slide 1