Lee Italy 2016 Final 1 - summerschool-aidi.it · Six-Sigma Design Product Center Product Redesign Design for Reliability and Serviceability ... moving average and RMS torque value

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www.imscenter.net © Copyright IMS Center, 2016. All Rights Reserved.

Recent Advances of Industrial Big Data Analytics

and Cyber Physical Systems for Smart Manufacturing and Service Applications

Jay Lee

Founding Director NSF Industry/University Cooperative Research Center on

Intelligent Maintenance Systems (IMS) Univ. of Cincinnati, Univ. of Michigan, Missouri Univ. of S&T, and Univ. of Texas Austin

www.imscenter.net

NSF I/UCRC since 2001


►  Changing Role of Data and Value Creation

►  Industrial Big Data Analytics, CPS, and Social Innovation

►  Examples

►  Conclusions

Outline

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The IMS Center


Global Industry Partners (80+)

CANADA • Syncrude

BELGIUM • FMTC

CHINA •  Foxconn •  CEPREI •  CEI •  Sinovel •  Beijing Shenzhou Software •  AITRI Shanghai •  Sany Heavy Industry

FINLAND • Kone

FRANCE • Alstom

GERMANY •  FORCAM •  Bosch/Rexroth •  Siemens •  Festo

HONGKONG • Metron Hongkong Ltd.

JAPAN • Denso • Omron Corporation • Hitachi • Nissan • Komatsu • Mitsubishi Heavy Industry

• Toshiba Corporation

KOREA •  Samsung Semiconductor •  Samsung (Electro-Mechanics •  KAU

SPAIN • Tekniker

TAIWAN •  HIWIN •  Cosen •  Advantech •  III •  PMC •  NTU •  Servtech •  CSD •  MIRDC •  PSi •  ITRI •  Delta Electronics •  Tongtai Machine Tool

BRAZIL • CETA/SENAI

USA •  P&G •  Allied Telesis •  NI •  Parker •  Goodyear •  Boeing •  GE •  Woodward •  API •  GCWW •  Applied Materials (2) •  LAM Research •  TI •  Micron •  Canrig •  Raytheon •  Emerson •  P&W Canada •  Intel •  Toyota •  Eaton • TechSolv •  Idaho Nat. Lab •  Ingersoll Rand •  Spirit Aerosystems •  Kistler • 

• SCK • HRL • Ford • Intel • Caterpillar • BorgWarner • Montronix • Daimler-Chrysler • Harley-Davidson • Johnson Controls

• Chevron •  ETAS • USPS • Festo • Cisco • Coherix • EDAptive

• Genex • Inteligistics • Prometec • Rockwell Automation

• Siemens TTB • Bosch • McKinsey & Co. • United Technologies

• We Energies

LEGEND: • Active Member • Past Member

CHINA •  China State Ship Co. •  Dongling Tech •  Shanghai Electric •  Haier •  Bao Steel •  Saanxi Heavy Truck

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IMS Vision and Grand Challenges

Productivity Transformation

Tools

Fail and Fix or

Fly and Fix Predict and Prevent

FAF PAP Near-Zero Downtime

(ZDT) Products and

Systems

Zero-Downtime (ZDT)


Enhanced Six - Sigma Design

Product Center

Product Redesign

Design for Reliability and Serviceability

Self - Maintenance

• Redundancy • Active • Passive

Health Monitoring

Communications • Tether - Free (Bluetooth)

• Internet • TCP/IP

Sensors & Embedded XYZ/ Connected XYZ

Product or System In Use

Just-in-Time Service

Smart Design Condition-based

Maintenance (CBM)

Smart Infotronics (Twin-Model) •  Decision Support Tools for Maintenance Scheduling

•  Asset Optimization Predictive and Prescriptive Analytics

Watchdog Agent® Degradation Assessment (Machine Feature Monitoring)

Health Information

Near “0” Downtime

Closed-Loop Life Cycle

Design

IMS Vision in 2000

NSF I/UCRC IMS Vision in 2000 www.imscenter.net

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GE Industrial Internet System

Ref: GE Industrial Internet

IMS Adventure in 2001 à Catalystà GE Venture in 2011




►  Examples

►  Conclusions

Outline

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Data to Knowledge

Data

Problems

Knowledge/Experiences


Product/Manufacturing Evolution

Avoid

Solve

Visible

Invisible

Improved Product Design

GE Power By the Hour On-Wing Suppor Komatsu Komtrax

GE Aviation John Deere Agri Service Goodyear Smart Tire

Improved Productivity/ Lean Manufacturing

(Self-Configure & Resilient Machine) Predictive Healthcare (Genetic Test and Analytics, Molecular Medicine)

Cyber-Physical Systems & Industry 4. 0 in Manufacturing

− Product/Services − Manufacturing

Smart Sensors & Prognostics and Health Mgt

Digital Manufacturing (Self-Aware Machine)

Predictive Big Data Analytics

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►  Examples

►  Conclusions

Outline


Industrial Big Data in Manufacturing

Source: IDC; McKinsey Global Institute analysis

Gaps for Analytics

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Value of Predictive Analytics

► Apply Data Analytics to large amounts of data of a variety of types to uncover hidden patterns, unknown correlations and other useful information from industrial and manufacturing systems and integrate with business automation software for improved productivity and innovation.

9/18/16 13


1. Broken 2.  Bad

3. Background

Challenges of Industrial Big Data

Jay Lee. Industrial Big Data Book (2015)

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•  Self-configure for resilience •  Self-adjust for variation •  Self-optimize for disturbance

V. Configuration

Level

•  Integrated simulation and synthesis •  Remote visualization for human •  Collaborative diagnostics and decision making

IV. Cognition Level

•  Twin model for components and machines •  Time machine for variation identification and

memory •  Clustering for similarity in data mining

III. Cyber Level

•  Smart analytics for •  Component machine health •  Multi-dimensional data correlation

•  Degradation and performance prediction

II. Data-to-Information Conversion Level

•  Plug & Play •  Tether-free communication •  Sensor network I. Smart Connection Level

Architectures for 5 Levels (5C) of CPS

A T T R I B U T E S

F U N C T I O N S

Jay Lee, Cyber Physical Systems, Wikipedia


Issues of Data Analytics in 2015-2020

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Cyber Manufacturing System •  Cyber Manufacturing is a transformative system that translates data

from interconnected system and people into predictive, prescriptive, and resilient operations in a collaborative environment.

Critical Component Risk AssessmentRadar Chart

TIME

RIS

K

PredictedIntervention Limit

TIME

RIS

K

Predicted

TIME

RIS

K

Predicted

BEARING

MOTOR

GEARBOX

Physical Space

Cyber Space

Cyber-physical Interface (CPI)

Fleet 1 Fleet 2 Fleet 3

Predictive & Prescriptive Tools

Closed-Loop Feedback

Data Visible Content

& Context

! Invisible

Issues


Cyber Machine Systems

Data Analytics

External: Sensors

(Image courtesy: http://www.huazhongcnc.com/en/Product/CNC-M/473.aspx)

Cyber Space

Physical Space Machine Self-Awareness

Closed-Loop Feedback

Module-Based Controller “App Store”

PHM Apps

Internal: Controller

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Smart Machine APPs


IMS Demonstration APP

List of Machines in factory plant with abstract information about each machine including: working status, Latest health value and last timestamp of historical data

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By clicking on each machine, detailed information of that machine will be displayed. In the first section (overview) a radar chart for overall health status of machine components is displayed



In components section, CV curve for each component will be displayed. User can select desired component to view its CV curve.

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►  Examples

►  Conclusions

Outline


Digital Threads of “Distributed Systems”

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What is a fleet? (1/2) ►  Scenario 1: For manufacturing, in one factory, there are equipment/

assets of the same type. These similar assets are referred to as a fleet.

Fleet: a group of similar machines/

assets in one factory.


What is a fleet? (2/2) ►  Scenario 2: Assets/machines of similar type that are experiencing similar

working conditions, while located at different factories, can also be considered as a fleet – focus of this research.

Individual asset/machine

Fleet: a group of similar assets/machines in

different factories.

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Fleet-based Health Monitoring Technique (2012) ►  The objective is to group similar machines for sound comparison

during fault detection.

Case Study: Robotic Arm Monitoring

Fleet Clustering

C

Local Cluster Fault Detection Local Cluster

Fault Detection Local Cluster Fault Detection Local Cluster

Fault Detection

Image courtesy: http://imgkid.com/industrial-robotic-arms.shtml

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0.4

BW_FMR4201

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Local Cluster Robot Condition Monitoring Fleet Clustering

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DAQ: FCFT

Performance Benchmarking with Independent Model:

Space of Improvement:

Dynamic Clustering


Robot Health Monitoring Nissan Manufacturing Plant

Smyrna, Tennessee 2010-12

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►  Applying the IMS logistic regression algorithm included using the moving average and RMS torque value of the low-speed regime segmented data set as the two features used in training the model using the unacceptable (degraded) state data and acceptable (healthy) state data.

►  The results of applying this method for the third robot joint servo motor are shown above, which shows that early signs of degradation can be seen as early as 3 weeks in testing cycle 125 but failure does not actually occur until cycle 220.

Robot Health Assessment –Example

Health Assessment Result for Third Servo-motor Robot Joint

Degradation can be detected as early as 3 weeks before event!!! Actual failure

event


New Product --Factory Sentinel

IMS Spin-off Company—Predictronics 2012

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2016 IMTS Demo


IMTS Demo

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IMTS Demo


Wide Bandgap Semiconductors

Raleigh, NC

New Institutes Planned for 2016

Opentopiccompe++ons

Selectedtopiccompe++onssuppor+ngagencymission,usingagencyauthori+esandbudgets

Digital Manufacturing

& Design Chicago, IL

Advanced Fiber-Reinforced

Polymer Composites Knoxville, TN

Lightweight Metal Manufacturing

Detroit, MI

AIMPhotonicsRochester,NY

FlexibleHybridElectronicsSanJose,CA

ManufacturingUSAStatusand2016Plans

Addi>veManufacturingYoungstown,OH AFFOA-Fibersand

Tex>les,CambridgeMASmartManufacturing

LosAngeles,CA

Future Network Goal: 45 Regional Hubs

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UI LABS/DMDII Facility, Chicago IL GRAND OPENING on May 11 2015 94,000 square feet - digitalmanufacturinglab,instruc+onalandcollabora+onspace

ExampleIns>tute:DigitalManufacturing$70Mpublicinvestment,~$106Mmatchfromover140members


A partnership of world-class companies including: Top universities including: Proven talent from numerous

state, educational, and vocational institutions:

Hundreds of Small and Medium Sized Manufacturing Enterprises (SMEs) seeking to improve competitiveness:

A Strong Private-Public Partnership

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►  Examples

►  Conclusions

Outline


Invisible

Analytics

Avoid

Visible

Data

Solve

Transformation

Value Product

Evidence Experience

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IMS Major Impacts 1.  IMS is the “Catalyst” of GE Industrial Internet and Fanuc ZDT

Technologies.

2.  Ranked the highest Economic Impacts (1:270) by NSF Economic Impact Study Report in 2012. 3.  Won PHM (Prognostics and Health Mgt) Data Challenges Competition

for 2008 (1st and 3rd) ,2009 (1st, 2nd, 3rd), 2010 (3rd), 2011 (1st and 3rd), 2012 (3rd), 2013 (3rd, 4th, 5th). 2014 (1 st), 2015 (4th), 2016 (1st, 4th ).

4.  NSF ICorps Award (2012) for New Company Spin Off Predictronics. 5.  5 Spin-off Companies (U.S., Japan, Taiwan, China) 6. NI for IMS License Trials in 2011 & NI LabView Watchdog Agent® Toolkit Official Global Licensing in 2015.


www.imscenter.net

Thank You !

Google Jay Lee, Prognostics, E-Manufacturing, E-Maintenance, Dominant Innovation, Cyber Physical Systems

Documents

Lee Italy 2016 Final 1 - summerschool-aidi.it · Six-Sigma Design Product Center Product Redesign Design for Reliability and Serviceability ... moving average and RMS torque value