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Advanced Infotronics Technologies for

Smart Life Cycle SupportA Closed-Loop Acquisition

Jay Lee

Wisconsin Distinguished and Rockwell Automation Professor andDirector of Center for

Intelligent Maintenance Systems (IMS)

A National Science Foundation (NSF) Industry/University CooperativeResearch Center (I/UCRC)

www.imscenter.net

Simulation-based Acquisition/Advanced Engineering Environment Conference

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Medium

GPM

Low

GPM

TodayToday

Individual Skills

Internet-Assisted

DigitalTransaction

On Site

Remote

Electronics

e

TomorrowTomorrow

Trends in Maintenance

Ref: IBM

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Competitive Intelligence

Focus on“Affordability,

Survivability, andSustainability”

Focus on “Smart Service”

&Innovative Life Cycle Support.

Design ProductsManufacturing

andSupply Systems

After Market

ProducibilityPredictability Productivity

Core Intelligence

PollutionPrevention

Performance

Focus on“Velocity”

4Jay Lee

Evolution in Product, Manufacturing, and Quality

ProductFocus

Manufacturing Focus

QualityFocus

1990 2000

Product That

Thinks and Links(information &

computer intelligence)

EnterpriseIntegration

(agility)

Six-SigmaFor

Business Process(enterprise)

1980

FactoryAutomation(flexibility)

SPC, TQM, TPMfor Manufacturing

Process(factory)

Intelligent Mechatronics

(data & controlintelligence)

2010

ProductsThat Learn,

Grow, Reconfigure,and Sustain

(knowledge & distributed Intelligence)

Near-Zero-Downtime &Sustainability

and Asset Optimization(customers)

E-Logistics & E-Manufacturing

(velocity )

IMS Center

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PredictiveIntelligence toInformate Decision

The Business Model

Execution

DemandInventory Capacity

Inventory Capacity

Execution

Demand

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Infotronics Technologies intertwine advanced

information and electronics systems

intelligence and enable autonomous business

functions and objectives through the use of

internet and other tether-free technologies (i.e.

wireless, web,…)

Infotronics Technologies ?

IMS Center

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Current Industry Maintenance Practices

•Reactive >50-60 %

•Preventative >40-50 %

•Predictive <5%

5x

10x

Predictive Preventive Reactive

Cost of Maintenance

“ 60%“ 60%

of all planned maintenanceof all planned maintenance

is unnecessary.”is unnecessary.”

ARC AdvisoryARC Advisory GroupGroup

Fail & FixRoutine maintenance

Fix before Fail

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Monitoring

Sensors & Events

Product orSystemIn Use

Vision

PredictivePerformance

• Web-enabledmonitoring,Prognostics, anddiagnostics

• Agent for Data Mining

• Logistics IntegrationTools

• Asset Optimization

• Knowledge Mgt.

Acquisition

RobustDesign

Enhanced Six-Sigma Design

Design for ReliabilityAnd Serviceability

Product

Redesign

Web-enabled D2B™ Platform

Degradation Watchdog Agent™

Self Maintenance

RedundancyActivePassive

Communications

• Tether-Free(Bluetooth)

• Internet•TCP/IP

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– Watchdog Agent™ for Predictive Prognostics

– Web-enabled Smart D2B™ (device to business)Platform and Tools for Data Transformation,Optimization, and Synchronization

– Applied Wireless Technologies

4. Logistics Infotronics Agent (LIA)

IMS Core Technologies

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Need for Prognostics

Degradation

Process ?

Prognostics

Watchdog

?MODEL

?

Need On-Site Experts

Sensors Monitoring

Signal ProcessingDiagnostics

Action

Assessment

Remote Experts

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Watchdog Agent

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Time-Frequency MomentsTime & Frequency Domain

Confidence Value- CV Principal ComponentsMahalanobis Distances

Correlation of multiple signals intoPerformance Prediction- CV

Sensor signals

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Performance Index Model Based PrognosticApproach

Step3: Degradationtendency prediction

Step 2: Currentperformance calculation

Step 1: Performanceindex modeling

Using parameters fixedafter each maintenance:

Normaldata

Failuredata

)(}1Pr{ 1 xxrv

f=Probability of failure:

Using historydata:

Beginningsetting

Acceptablelevel

)()( 2 xxrr

fC =

Confidence:

Determine model structure ofprobability or confidence curve

With Box-Jenkins method

)()1()( 1 pkykyky p −++−= φφ L)()1()( 1 qkkk q −−−−−+ εθεθε L

On-line parameters ( )

identification, then predict

iφ,iθ

Failure Line

Predict failure probability orconfidence continuously

Currentinputs x

r

}1Pr{ xr

Failureprobability

)(xvC

Confidence

M

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Mean Time Between Degradation

Trigger pointPredicted degradation

time

CV1

Degradationthreshold

MeanTimeBetweenDegradation

∫∞

⋅0

)( dttPt

time

MTBD =

15

0 50 1 0 0 1 5 0 2 0 0 2 5 0 3 0 0 3 5 0 4 0 00

0 . 5

1

T i m e / d a y s

Prob

abil

ity

of f

ailu

re

0 50 1 0 0 1 5 0 2 0 0 2 5 0 3 0 0 3 5 0 4 0 01

1 . 5

2

2 . 5

3

T i m e / d a y s

Max

spe

ed/m

ps

0 50 1 0 0 1 5 0 2 0 0 2 5 0 3 0 0 3 5 0 4 0 02

2 . 5

3

3 . 5

4

T i m e / d a y s

Cyc

le T

ime/

s

m a i n t e n a n c e

m a i n t e n a n c e

m a i n t e n a n c e

po in t1 po in t2

Fa i lure Line p red ic t ion2 p red ic t ion1

Example

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System/ Machine

Hybrid Prognostics and Diagnostics

PerformanceStatusWhen

degradationoccur

Product NervousSystems

Look uptables

PerformanceMeasure

Hashing

Degradation (Prognostics)

signal fordegradation

Production

Sensors

whenVirtual Instruments

FusionDiagnostics

+Prognostics

wherewhat

why

Fusion

Diagnostics

Identifyreasons

Internet

Neural NetworkGenetic AlgorithmStatistic ComputingFuzzy Logic…..

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MACHINE 2

Similarity Matrix for Performance Comparison

.75

--

G

--

HFEDCBA

00.140.750H

-00.29010G

--.670100F

--.11.670.14E

--0.29.60D

--00C

--0B

--A

MA

CH

INE

1

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Simulation of Throughput Response SurfaceBetween Parameters

0.90.95

10.9

0.95

1

136

138

140

142

144

146

148

150

0.9

0.95

1

0.9

0.95

1

115

120

125

130

135

140

145

150

Sensitivity

Degradation of (X1 & X2)

Sensitivity

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Watchdog Toolbox

Watchdog ToolboxJoint T-F Method

Applied Statistical MethodsHybrid Techniques

CMAC LearningMethod

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D2B™ PlatformEmbeddedMonitoring

Prediction & Performance Assessment

Optimization

Synchronization

e-Business,e-Manufacturing,

e-ServiceAutomation

Optimization ofProduction,

Delivery,Maintenance,

Human Resources,

Logistics Systemsetc.

DATA/INFORMATIONDevice/Factory Level

Transformation intoKNOWLEDGE

Real TimeDECISION/

Synchronization

Web

Data/Information Transformation XML

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Applied Wireless Issues

• Communication Network

– Wireline vs. Wireless

– Performance -Throughput vs. Latency

– Reliability -Control vs. Monitoring

– Cost - ISM Band

• Wireless Technologies

– IEEE 802.11

– Bluetooth (IEEE 802.15)

– Proprietary

Attenuation (dB)

STA

AP d

Building Layout - Sheet Rock Walls

Attenuation (dB)

Building Layout - Cinder Block Walls

Radio Propagation Issue

Prof. Ivan Howitt, EE UWM

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Tether-Free Embedded Infotronics Agent

HiDRA™ from Rockwell Scientifics

HiDRA™

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Remote Monitoring and Prognostics ofDefenses Equipment

For short distancetransmission:- 802.11- Bluetooth- etc.

Cellular phones or GPS for longrange transmission

AlarmsPrognostics

Remote diagnosticsService Dispatching

Synchronization

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Logistics Infotronics Agent (LIA)

Usage, Service & Maintenance

1. Initial product info is written in the product

embedded device

Product delivery

Internet

LogisticsInfotronics Agent (LIA)

Producer’sProduct Life

CycleIntelligence (LCI)

System

5. Design Innovation through life cycle

intelligence

4. Update product usage information in the Life Cycle Monitoring System

3. Wireless Internet connection

between mobile device and

producer’s product life cycle system2. Wireless

bluetooth connection between mobile

device and product embedded device

Tether-FreeTransmission

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Logistics Infotronics Agent

(LIA)

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Final ProductAssembly and

Testing

Supplier 1e-Shops

Enterprise Database

Mgt System (EDMS)(design, manufacturing, suppliers, testing, etc)

Supplier 2e-Shops

Supplier Ne-Shops Sourcing

Life Cycle Service &Maintenance (LCSM)

Supplier Evaluation& Selection

….

Dec

isio

n

e-Procurement

Manufacturing Enterprise Execution System (MEES)

Product, Process, and EnterpriseDesign (PPED)

CustomerNeeds

Today’s Acquisition and Logistics Systems

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Final ProductAssembly and

Testing

Supplier 1e-Shops

Logistics InfotronicsAgent (LIA)

Data Accessibility&

Monitoring

EnterpriseDatabase

Mgt System (EDMS)(design, manufacturing,suppliers, testing, etc)

Product, Process, and EnterpriseDesign (PPED)

Supplier 2e-Shops

Supplier ne-Shops Sourcing

Life Cycle Service &Maintenance (LCSM)

Supplier Evaluation& Selection

….

Dec

isio

n

e-Procurement

Manufacturing Enterprise Execution System (MEES)

Tether-free Interface

Prediction, Validation & Optimization

CustomerNeeds

Design for Six-Sigma(DFSS)

SupplierQuality

Tether-free Interface

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• National Instruments

• ITRI• PMC

• Kone Elevator• API• Genex Technologies

• ATOP

• Dr. Machine.Com

• Hitachi Seikei

• Velicon

• Eagle Technologies

• Industrial Objects

• InterNext Group

• Citation Custom Products

Current Members and Sponsors

• Rockwell Automation• Ford Motor• GM• Johnson Controls• Eaton• Harley-Davidson

• Xerox• Wisconsin Electric Power• United Technologies• Siebel Systems• GE Medical Systems• Toshiba (Japan)• A.O. Smith• Intel• U.S. Postal Services

• DaimlerChrysler

• Lantronix

• Micro Mobio Wireless

• Cognex

• Boeing

• DoD DMSO

• Motorola

• ABB

• Caterpillar

• SEMATECH

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Testbeds andProjects

CompanyMembers

Knowled

ge, B

est P

racti

ces,

Tools

, and

Stud

ents

IMS Methodologies and Enabling Technologies

Testbeds andProjects

Value through Leveraged Partnerships

Membership and Partnerships

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Why IMS?

IMS Methodologies

Fail and Fix orFly and Fix Predict and

Prevent

FaF PaP

IMSTestbeds

(e-Maintenance/e-Manufacturing/

e-LogisticsIntegration)

Partnerships

Risk Reduction, Technology Readiness,

andNew Breed of

Engineers and Workforce

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For More Informationwww.imscenter.net

Thank You