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Advanced Prognostics for Smart Systems Methodology, Tools, and Applications
NSF I/UCRC since 2000
Jay Lee
Ohio Eminent Scholar and L.W. Scott Alter Chair ProfessorUniv. of Cincinnati
Director NSF Industry/University Cooperative Research Center on
Intelligent Maintenance Systems (IMS)www.imscenter.net
Outline
• Business and Productivity Models• Prognostics Design: Methodology and Tools• Design of Prognostics Platform for Various
Applications• Design of Smart Service Systems
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Outline
• Business and Productivity Models• Prognostics Design: Methodology and Tools• Design of Prognostics Platform for Various
Applications• Design of Smart Service Systems
IMS Vision and Mission Statement
To enable products and systems to To enable products and systems to achieve and sustainachieve and sustain nearnear zerozeroachieve and sustain achieve and sustain nearnear--zero zero breakdown performancebreakdown performance, and ultimately , and ultimately transform machine condition data to transform machine condition data to useful information for improved useful information for improved productivity and asset utilization.productivity and asset utilization.
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The IMS Consortium
Common Unmet Needs in Future Maintenance and Service
Intelligent Monitoring, Predict and Prevent for
Guaranteed SustainabilitySelf-Assessment
Autonomous InformationFlow from Field (user)To Service/BusinessSystems
OperationsIntelligence
Only Handle Information Once (OHIO)
Predict, Prioritize, Optimize, and Plan Maintenance SchedulingFor Near-Zero Downtime and
All-Time Readiness
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Outline
• Business and Productivity Models• Prognostics Design: Methodology and Tools• Design of Prognostics Platform for Various
Applications• Design of Smart Service Systems
Service Design using “System-of-System” Approach
Design for Reliability and Serviceability
Health MonitoringSensors & Embedded
Intelligence
Product orSystem
In UseJust-in-Time
Service
Near “0”Downtime
Loop
Design
Closed-LoopLife CycleDesign
E h d
ProductCenter
ProductRedesign
Self-Maintenance•Redundancy•Active•Passive
Communications •Tether -Free(Bluetooth)
• Internet•TCP/IP
IntelligenceIn Use Service
SmartDesign Condition-based
Maintenance(CBM)
• Web-enabled Monitoring & Prognostics
• Decision Support Tools for Optimized Planning &Scheduling
• Responsive Services
Degradation Assessment(Feature Monitoring)
EnhancedSix-SigmaDesign
•TCP/IP p
• Asset Optimization
Health Information
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Principal ComponentsPerformance Feature
System Instrumentation ApproachSystem Instrumentation Approach
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7.5
Prediction of Machine Performance Life
Normal Behavior
Most Recent
Behaviorr
Health Feature Radar Chart
1 Bearing
200 210 220 230 240 250 260 270 280
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3.5
4
4.5
5
5.5
6
6.5
Cycle Number
Prin
cipa
l Com
pone
nt
Actual DataPredictionActual DataPrediction Uncertainty
00.20.40.60.8
1 Bearing
Tools for Autonomic Computing
User input
6
ture
f2
Example of two dimensions spaceFailure
Mode #1
Failure Mode #2
Safe operation
Statistical Pattern Recognition• What?
Analytical comparisons of feature distributions
• When?
0Feature f1
Feat Mode #2
Cluster distribution
CV C t
Cluster displacement
When?Features are (approximately) GaussianStable operation without too many impacts
• Possible application:
Time
1
0.5
0
CV Current time
Evaluation the overlap area yields the CV
ppRepeatable processes and operations
Methodology
Smart• Assess Health Degradation• Predict Performance Trends
Streamline• Sort, Filtering, Prioritize Data• Reduce Sensor Data Sets & PCA• Correlate and Digest Relevant Data
Smart Processing
Predict Performance Trends• Diagnose Potential Failure (Prognostics)
Synchronize
• Embedded Agent (hierarchical and distributed)• Only Handle Information Once• Tether-free Communication• Decision Support Tools
Standardize• Systematic Prognostics Selection• Reconfigurable Hardware Platform Toolbox
M i t I f ti St d di ti• Maintenance Information Standardization
Sustain• Embedded Self-Learning & Knowledge Mgt. • Closed-Loop Life Cycle Design• User-Friendly Prognostics Deployment
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7
Data Streamlining
Streamline
Expert k l d
MaintenanceDatabase
No Maintenance
Record
Data Cleaning, Fil i d S iSmart
Processing
Synchronize
St d diData Collection
Expert Knowledge
knowledge Filtering and Sorting
Identify Critical Components
Standardize
Sustain
Data Cleaning, Dimension Reduction
Determine a Clean and Reduced Data Set
IMS Methodology: 5S Approach
• Assess Health Degradation
Streamline• Sort, Filtering, Prioritize Data• Reduce Sensor Data Sets & PCA• Correlate and Digest Relevant Data
Smart Processing
g• Predict Performance Trends• Diagnose Potential Failure (Prognostics)
Synchronize
• Embedded Agent (hierarchical system)• Tether-free Communication• Only Handle Information Once (OHIO)• Decision Support Tools
• Systematic Prognostics ImplementationStandardize
y g p• Reconfigurable Hardware and Software
Platform• Maintenance Information Standardization
Sustain• Embedded Knowledge Mgt. for Self-Learning• Closed-Loop Product Life Cycle Design• User-Friendly Prognostics Deployment
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Watchdog Agent ToolboxSignal Processing & Feature Extraction
Time-frequency / Time-frequency
Performance Assessment
Logistic Regression
Performance Prediction
M t h M t i
ARMA Prediction
moments
Wavelets/Wavelet packet energies / Wavelet moments
Autoregressive modeling / AR model roots
Statistical Pattern Recognition
Feature Map Pattern Matching
CMAC Network Pattern Match
Match Matrix
Elman Recurrent Neural Network
Fuzzy Logic
Health Diagnosis
Expert extracted features
?
Time
1
0.5
0
CVCV
Pattern MatchSupport Vector
MachineHidden Markov
Model
Bayesian Belief Network
Fast Fourier Transform
Information Delivery
Results of Smart Prognostics Tools for Asset Health Information
Confidence Value Health Map for Health Radar Chart
00.20.40.60.8
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Risk Radar Chart for performance degradation assessment(0-1)
ppotential issues and pattern classification
for multiple components degradation monitoring
toPrioritize Maintenance Decision
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Information Delivery
Results of Smart PrognosticsTools for Asset Health Information
Confidence Value Health Map for Health Radar Chart
00.20.40.60.8
1
Risk Radar Chart for performance degradation assessment(0-1)
ppotential issues and pattern classification
for multiple components degradation monitoring
toPrioritize Maintenance Decision
Field Service Application: KONE Testbed
Equipment Elevator Door
(Controller)
Remote Monitoring
E d
OPC Server
WAGO I/O
iCONICS Genesis32(OPC client)
IIS web server
Web Browser
Bridge
BusinessApplications
Encoder
Watchdog Database
iCONICS GenBroker
(COM over tcp/ip)
Bridge
D2B™
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A Case Study: Elevator Door A Case Study: Elevator Door TestbedTestbed
Performance Indicators
Normal Operation
CV = 97.6%
Normal Operation
CV=97.6%
• Cycle time of the elevator door
• Maximal speed of the elevator dooe
Indicators
Increased Friction
CV=70.3%
Information Delivery
Results of Smart Prognostics Tools for Asset Health Information
Confidence Value Health Map for Health Radar Chart
00.20.40.60.8
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Risk Radar Chart for performance degradation assessment(0-1)
ppotential issues and pattern classification
for multiple components degradation monitoring
toPrioritize Maintenance Decision
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ESMD Chillers FCFT Predictive Maintenance for
Hong Kong International Airport Control TowerHong Kong International Airport Control Tower (May 2007)
Project Background
Chiller at Control Tower of H.K. Int. Airport
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Hardware Setup
Chiller with 6 AccelerometersDetails:IMI 623C01, high-freq., ceramic shear ICP accel, 100mV/g, 0.8 to 15k Hz, top exit, 2-pin conn.From: S&V Samford Instruments Ltd.
NI PXI Data Logging SystemDetails:- PXI-4472, 24 Bit, 102.4 kS/s, 8 Inputs, 110 dB Dynamic Range- 6 SMB to BNC connectors, SMB-100, SMB to BNC Female- Communicate with Data Logging Server through NI MXI-4 technology
Johnson ControlsOPC Server
35m BNC Cable x 6
3m copper cable3m LAN cable
Data logging ServerDetails:- Dell Dimension(TM) E520- Data Logging System
3m copper cable
Raw Data
Vibration signals OPC (OLE for Process Control) Data
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Fixed Cycle Feature Test (FCFT)
• How to convert raw data to information?
T2 T3information?
• Why FCFT?• What is FCFT?
T1
T – transition of the load change
FCFT Radar Chart
EMSD vibration signals on 2007-05-21 at 16-03-54 ----------- Normal
Data ---- Information
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FCFT Radar Chart
EMSD vibration signals on 2007-05-22 at 10-57-04 ----------- Normal
FCFT Radar Chart
Some mechanical faults happened at that time.
EMSD vibration signals on 2007-05-14 at 11-22-18 ----------- Abnormal
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Information Delivery
Results of Smart Prognostics Tools for Asset Health Information
Confidence Value Health Map for Health Radar Chart
00.20.40.60.8
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Risk Radar Chart for performance degradation assessment(0-1)
ppotential issues and pattern classification
for multiple components degradation monitoring
toPrioritize Maintenance Decision
KomatsuKomatsu
Earth Moving Equipment Field Services
(Komatsu Engineer on UC Campus)
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Komatsu’s Remote Maintenance• Komatsu Ltd. has developed the necessary
infrastructure for collecting data from individual construction machines, from the
– Failure History data• Failure code, Occurrence date & time,
Repaired date & time– Trend Analysis Data
D t f ( / / i
Help deskplace of operation via the user
• Data from sensors (as max/ ave/ min every 20 operating hours)
– Others data• Load meter, Running time of engine,
etc.
Mode m Bank
Mode m Bank
Mode m Bank
• Failure prediction & diagnosis failures– to reduce down time, maintenance & support
costs
Focus on Smart Asset Business
• by detecting incipient degradations of all components• by identifying and isolating failure critical components
• Degradation monitoring of engine and transmission systems– to estimate lifetime with higher accuracy
t t d l f il– to prevent or delay failures– to expand components overall life
• using appropriate and key data• using features for key performance index
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Komatsu Approach
– to increase quality of data in Komatsu database
– to enable more advanced predictive maintenance decision making base on analytical reliability models
– to achieve intelligent maintenance excellence
Sensors
IMS WatchdogTM KOMATSU On -
Board Computer– and to implement knowledge based decision making approach
Board Computer
Monitoring & Aid View to DecisionEngine DT09XXN Replacement, High priority
Engine DT09XYH Replacement, High priority
Engine DT43XZA Maintenance requiredPlan replacement in 1500 h
Engine DT77YVP Good, 5000 h remaining
Engine
Comp.A,C
Comp.D
No change
IMS Embedded Smart Software Tools
ENGINEERING KNOWLEDGE AND DATAACQUISITION FROM THE SYSTEM
Komatsu WebCARE
Customer Appliance
Komatsu Engineers
Signal Processing&
Feature Extraction
Health Assessment&
Prediction
Bayesian BeliefNetwork (BBN)
C
Autoregressive Modeling
Match Matrix & ARMA Modeling
Group Methods Data Handling Based Feature Extraction
B
Fuzzy Logic (FL) Based Decision Making
D E
Data Preparing (Filtering Outlier estimation)
A
Human MachineInterface
AUTOMATIC AID TO DIAGNOSIS DECISION MAKING
AUTOMATIC AID TOPROGNOSIS DECISION MAKING
Remaining Lifetime Prediction Comparison and Predictive Maintenance Planning
F
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Failure Mode Effects & Risk Assessment Tools
Decision Support Tools for Smart Service Recommendation
FL or Reliabilitybased Prognostics
WearCrackingCable disc.Sensor failsInjectorWear M Change 0.9Derailment Fall of the Shaft 1
CylinderInlet Valve RInlet Valve LInjector…
4 0.6 H Change engine
S Prognostic
Machines
Effect on the system
Machine B
Engine AXMachine A
CCauses
2FM 1
FM 2
Fall of the cabin
Transmit. BY
Component D PSystem ref. Engine ref. Failure Mode
CrackingSensor fails
D: Detectability C: CostP: Probability S: Severity
Bearingof the cabin cabin GearsBearing
BBN output Decision Aid
D
0 40.60.8
1
Autonomic Service Logistics
Valve
Bearing Logistic Information
•Engine/Asset Location
00.20.4Valve
Cylinder
•Components Availability
•Review Previous Maintenance Actions
•Recommend and Assign Right Technician
•Estimate Maintenance•Estimate Maintenance cost
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Information Delivery
Results of Smart Prognostics Tools for Asset Health Information
Confidence Value Health Map for Health Radar Chart
00.20.40.60.8
1
Risk Radar Chart for performance degradation assessment(0-1)
ppotential issues and pattern classification
for multiple components degradation monitoring
toPrioritize Maintenance Decision
Bearing Fault BackgroundNormal Roller Inner-race Outer-race
Outer + inner Outer-race Outer + inner Outer race +roller + roller + roller
Drawing courtesy of Wikipedia
Normal condition Outer-race failure Roller failure Inner-race failure
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Feature Extraction
FFT (Fast Fourier Transform)
Overall acceleration, velocity, or displacement, amplitude peaks at1X RPM, 2X RPM, 3X RPM, BPFO, BPFI, FTF and BSFHarmonics and sidebands, RMS and kurtosis
Health Assessment - Multi-fault Data
Inner- race
Roller
Outer- race
Normal Condition
Outer Race + Roller
Outer + Inner + Roller
Outer + Inner Race
Inner- race + Roller
SOM accomplishes two things: reduces dimensions and displays similarities.
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IMS Methodology: 5S Approach
• Assess Health Degradation
Streamline• Sort, Filtering, Prioritize Data• Reduce Sensor Data Sets & PCA• Correlate and Digest Relevant Data
Smart Processing
g• Predict Performance Trends• Diagnose Potential Failure (Prognostics)
Synchronize
• Embedded Agent (hierarchical system)• Tether-free Communication• Only Handle Information Once (OHIO)• Decision Support Tools
• Systematic Prognostics ImplementationStandardize
y g p• Reconfigurable Hardware and Software
Platform• Maintenance Information Standardization
Sustain• Embedded Knowledge Mgt. for Self-Learning• Closed-Loop Product Life Cycle Design• User-Friendly Prognostics Deployment
Synchronization System Structure
InternetHealth InformationData flow
HealthInformation
Display
ATC StatusDisplay
Discrete EventDisplay
User
Interf
Embedded side Remote side
......
Error LogDatabase
AlgorithmModification
Data Format
ace
Industrial FieldRaw data
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IMS Methodology: 5S Approach
• Assess Health Degradation
Streamline• Sort, Filtering, Prioritize Data• Reduce Sensor Data Sets & PCA• Correlate and Digest Relevant Data
Smart Processing
g• Predict Performance Trends• Diagnose Potential Failure (Prognostics)
Synchronize
• Embedded Agent (hierarchical system)• Tether-free Communication• Only Handle Information Once (OHIO)• Decision Support Tools
• Systematic Prognostics ImplementationStandardize
y g p• Reconfigurable Hardware and Software
Platform• Maintenance Information Standardization
Sustain• Embedded Knowledge Mgt. for Self-Learning• Closed-Loop Product Life Cycle Design• User-Friendly Prognostics Deployment
Watchdog AgentWatchdog Agent®® Software PlatformSoftware Platform
Executive Agent
Task Scheduler ISO 13374 /Engine
AlgorithmToolbox
Algorithm Selector
Health Diagnosis
PerformanceAssessment
PerformancePrediction
Feature Selection
Processing TaskISO 13374 /OSA-CBM
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CommunicationsToolbox
Database
Agent External Devices
Services
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D2B™ Machine Level Connectivity Sensor Fusion
Feature Extraction
Reconfigurable Prognostics Platform
Performance AssessmentAutonomous Learning
Prediction and PrognosticsDecision Support Tools
D2B™ Business Level Connectivity
IMS Prognostics Platform
IMS Methodology: 5S Approach
• Assess Health Degradation
Streamline• Sort, Filtering, Prioritize Data• Reduce Sensor Data Sets & PCA• Correlate and Digest Relevant Data
Smart Processing
g• Predict Performance Trends• Diagnose Potential Failure (Prognostics)
Synchronize
• Embedded Agent (hierarchical system)• Tether-free Communication• Only Handle Information Once (OHIO)• Decision Support Tools
• Systematic Prognostics ImplementationStandardize
y g p• Reconfigurable Hardware and Software
Platform• Maintenance Information Standardization
Sustain• Embedded Knowledge Mgt. for Self-Learning• Closed-Loop Product Life Cycle Design• User-Friendly Prognostics Deployment
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Next Step Close the Loop
Design for Reliability and Serviceability
Health MonitoringSensors & Embedded
Intelligence
Product orSystem
In UseJust-in-Time
Service
Loop
Design
Closed-LoopLife CycleDesign
E h d
ProductCenter
ProductRedesign
Self-Maintenance•Redundancy•Active•Passive
Communications •Tether -Free(Bluetooth)
• Internet•TCP/IP
IntelligenceIn Use Service
SmartDesign Condition-based
Maintenance(CBM)
• Web-enabled Monitoring & Prognostics
• Decision Support Tools for Maintenance Schedule
• Synchronization Service
Watchdog Agent™Degradation Assessment(Feature Monitoring)
EnhancedSix-SigmaDesign
•TCP/IP
Watchdog Agent is Trademarks of IMS Center
• Asset Optimization
Health Information
Int. IMS Project PROMISE – (Product Embedded Information System for Service and EOL)
1. Initial product info is written in the product embedded device
Product delivery Producer’s
KPDM
Internet
Embeddeddevice 3. Wireless Internet
connection between mobile device and producer’s KPDM
3. Wireless bluetooth connection between mobile device and
product embedded device
Bluetooth ?
Service &maintenance EOL
2. A certified agent for service or EOL operations4. Update information in
the embedded device and in the producer’s KPDM
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Outline
• Business and Productivity Models• Prognostics Design: Methodology and Tools• Design of Prognostics Platform for Various
Applications• Design of Smart Service Systems
Prognostics Design ad Development
Smart
Streamline
Smart Processing
Synchronize
Standardize
50
Sustain
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Commercial Hardware -1
UNO-2160 Watchdog Agent® System
High Performance and Rugged
– Fanless Celeron 400MHz CPU– 256/512MB SDRAM
9 30VDC i t
Non-volatile Storage Media– CompactFlash– 2.5’’ HDD– Battery Backed RAM
Built-in Comm. Interfaces– 2x Ethernet ports
– 9-30VDC power input– -10 to 50C operating temp– Hardware Watchdog Timer
– 4x Serial ports– 2x USB ports– 1x Printer port
Expansion Capability– 1x PC Card slot (PCMCIA)– 2x PC/104 slots
Robust Embedded OS– Windows CE.NET– Windows XP embedded
Commercial Hardware -2
I/O– Analog Input/Output,
Digital Input/Output– (Low / high speed,
simultaneous /
CompactRIO reconfigurable embedded system
simultaneous / multiplexed)
Real-time controller– 200 MHz Pentium Class– 64 MB or 512 MB FlashReconfigurable Chassis– 4-slot or 8-slot 1 M Gate or
3M Gate FPGAConnectivity– Standard connector
depends on the module– Options: Strain Relief
Connector Block, Custom Cable, etc.
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Watchdog Agent Tools on LabVIEW
IMS Watchdog Agent® in LabVIEW
LogisticRegression
PerformanceAssessment
FeatureMap
CMACPattern Match
PerformancePrediction
Match Matrix
NeuralNetwork
HealthDiagnosis
HiddenMarkov Model
BayesianNetworks
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IMS Plug-n-Prognose System
Watchdog Agent®
Toolbox Hardware Platform
+Robust Health
Information
Radar Chart for Components Health Monitoring
Confidence Value for Performance Degradation MonitoringData
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Health Map for Pattern Classification
Risk Radar Chart to Prioritize Maintenance Decision
Watchdog Agent®
Platform
Embed Prognostics in Controller
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Validated Hardware
• Allen-Bradley ControlLogix/1756 Rack withLogix 5550
• Special Application Module for ControlLogix – Intel Pentium 266MHz CPU, 128MB of RAM and
Windows XP Professional as operating system– Communication between 56SAM module and
processor is realized over the backplane of the rack
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Smart Machine Testbed(IMS/TechSolve in Cincinnati, OH)
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Smart Self-Maintenance Machines
MTHI0. 690
MTHI0. 925
MTHI0. 560
MTHI MFPIConstraints MFHI
ProductM1
MTHI0. 751
1. Schedule2. Budget3. Quality
Tolerances4. Etc…….
0.745
Product Quality
Downtime
Maintenance
Flexibility
Readiness
EnergyM2
M3
M4
M5
M6
Prognostics Tools for Vehicle Telematics• Oil pressure and Oil levels
• Fuel level
Od t di• Odometer reading
• Vibration and Acoustic data
• Air Pressure in tire and Flat tire
• Condition of air bags
• Box itself
• Different points on engine and transmission
• Anti brake system
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Historical Working
FeaturesObtain data from the critical locations
Feature extractionSmart Bridge Health Prognostics
Historical Data Embedded
Sensors
Working Condition
In feature spaceNormal Behavior Most
Recent B h i
Health assessment
Behavior
00.20.40.60.8
1
Visualization by performance chart
Beam 1
Truss 1
Beam 2
Truss 2
Support 2
Support 1
Future Combat Systems (FCS) and Needs
Condition-based NeedsIMS Smart Watchdog Agent® Maintenance Platform
PrognosticsRemote diagnostics
L i ti S h i tiLogistics Synchronization
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Self-Maintenance Systems
ModelExpertKnowledge
Performance
Degradation
Model Knowledge
Prognostics
Assessment
Diagnostics
IMSWatchdogAgent™
Sensors
Degradation Information
• Near-Zero-Downtime Service• Autonomous Service Request for Spare Parts
• Asset Optimization
DecisionSupport Tool
Monitoring of Standard Components, e.g.,
Components, e.g.,
Components, e.g.,
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Outline
• Business and Productivity Models• Prognostics Design: Methodology and Tools• Design of Prognostics Platform for Various
Applications• Design of Smart Service Systems
Service Needs for Tomorrow:
• Service is a Customer-Intensive SystemService using a System Instrumentation (Smart• Service using a System Instrumentation (Smart Agent)
• Service through Smart Operation Analytics• Service for Knowledge Management Business• Service to Avoid Potential Issues for Customers.
J. Lee, NSF Symposium on Technology Management in the Service SectorPortland, OR, August 5, 2007 (Co-Sponsored by IBM Service Science)
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Transformation of Service Intelligence
Infotronics Technologies
Synchronized Value Chain Flow
(Cash Flow)
ConclusionsConclusions
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Our Approach Our Approach Value FlowValue Flow
Risk
Validation Company
IMS Platform
DecisionSupport
Watchdog Agent™
IMS Projects Testbeds
ContractsMembership
Research Tools
Validation(Shared Tools & Knowledge)
p y
Designated Projects
and
Implementations
Support Tools
Value
CompanyMembers
Membership and Partnerships
Membership Value through Leveraged Partnerships
Testbeds andProjects
IMS Methodologies
Testbeds andProjects
25:1
IMS Methodologies and Enabling Technologies
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RISK&COST
Today
IMS Value Proposition –Rapid Prognostics Design and Deployment
TIME
RISK&COST
Saving
Value
Risk and Cost Reduction Strategy TIME
IMS Global Research PartnershipsLulea UnivSweden
EPFLSwitzerland
TU-BerlinGermany
Univ. of Toronto,Canada
Shanghai Jiao Tong Univ., China
Beijing Univ of ChemicalS&T (BUCT)
ITRI & PMC
Univ. of TokyoJapan
CRCIntegrated Asset Eng.QUT, Australia
SIMTechSingapore
SENAI/UFRGSBrazil
UTTFrance
Cranfield UnivUK
H.K.PolyU
Taiwan
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Thank You Thank You !!For More InformationFor More Information
Please visit:Please visit:www.imscenter.net
&www.dominantinnovation.com
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