Artificial Intelligence for Industry 4.0

0 copy Copyright 2017 FUJITSU

FujitsuForum2017

FujitsuForum


Artificial Intelligence for Industry 40

Dr Fritz Schinkel

Head of Competence Center Big Data

Fujitsu


Automation of Big Data Value Chain

Big Data

CollectStream

Structured amp unstructured data

Devicessensors

Internet of ThingsCleanse Transform

Model LearnAnalyze

FindDecideNavigate

Research amp development science

Operation automation

production

Interactive reporting advertising

Rapid modelling for faster insights

Social media open data linked data


Challenge for Industry 40Individualized Production Individualized Analysis

Individualized

Production

Lots of

different

models

Variety of

processes

Machine

learning


Programming vs Machine Learning

Data

Computer

Result

Data

Computer

Result

Program ModelHistorical

data


Zinrai The Fujitsu Framework for AI

ee p learn ing

Neuroscience

Machine learning

Social receptivity Simulation

- Image recognition

- Voice recognition

- Emotionstate

recognition

- Natural-language

processing

- Knowledge processing

amp discovery

- Pattern discovery

- Inference amp

Planning

- Prediction amp

optimization

- Interactivity amp

recommendation

Human Centric AI

Sensing and Recognition Knowledge Processing Decision and support

Learning

AdvancedResearch

Deep Learning Machine Learning Reinforcement Learning

Neuroscience Social Receptivity Simulation

People Businesses SocietyActuationSensing

- Image recognition- Voice recognition- Emotionstate

recognition

- Natural-languageprocessing

- Knowledge processingamp discovery

- Pattern discovery

- Inference amp Planning- Prediction amp optimisation- Interactivity amp

recommendation

Proven through more than 300 AI-related business projects


Example k-Means Clustering

Find groups of similar individuals (cluster)

Get small distance of individuals to cluster center

Start Color Mean Color Mean Color Mean

=

Stop

when no

changes

Color like

closest cross

Move crosses

to centers of

groups

Position k

colored

crossesIterate


Standstill Analysis in Productions Processes


Overall Equipment Effectiveness (OEE)

Improve production and maintenance planning

Differentiate important types of down time

Use machine data recording from production

Understand processes level from machine data

Total Calendar Time

Total Scheduled Time Loading

Available Production Time

Running Time

Net Operation Time

Productive Time Quality

loss

Perform

loss

Availability

loss

Planned

downtime

Main-

tenance

No

material

Adjust-

ments

Tool

exchange

UpdatesMicro

stoppage

Break-

down

Breaks

weekends


Black amp White Solving Problem in Two Ways

White box model

Comprehensive study and data collection

Detailed Understanding of all parameters

Model construction on combined parameters

Black box model

Observation of basic parameters

Grouping of similar stillness events

Model construction on prototypical events

Low modelling effort

Model transferability

Unexpected learnings

Precise evaluation

Low compute effort


White Box Process Analysis

Program status Machine status Position Durations

Data selection Analysis of

parameter developing

Process knowledge essential

Modeling Transform visual

understanding into formulas

Calculation

time

Break

Adjustment

Tool exchange

Prog

ram

sta

tus 119872119894119888119903119900 119904119905119900119901119901119886119892119890 = 119905119904119905119886119903119905

119905119890119899119889 120594119904119905119886119905119906119904_6(119905)119889119905

= 119894=0119873minus1120594119904119905119886119905119906119904_6 119905119904119905119886119903119905 + 119894 lowast 001119904 lowast 001119904

with 120594119904119905119886119905119906119904_6 as characteristic function of times with Status 6

120594119904119905119886119905119906119904_6 119905 = 1 119891119900119903 119904119905119886119905119906119904 119905 = 60 119891119900119903 119904119905119886119905119906119904(119905) ne 6

and N the number of time intervals for a resolution of 001 s

N =119905119890119899119889minus119905119904119905119886119903119905

001119904


Black Box Clustering the Observation

Observable position

Duration

Data selection Few clusters

Clear segmentation

Clustering Sort by relevance

Identify expected

outcome

Learnings from the

unexpected

Interpretation

Silhouettes

00 05 08

Between -1 and 1

High is goodSta

ndstill

clu

ste

rs

Costs for k=567815Cluster Index

Dura

tion

100000

10000

1000

100

10

1

01


Interpretation of Clusters

1 Long duration (days) Breaks

2 Short duration (seconds) scattering production

6 Focused position and duration ~10 seconds tool exchange

4 Focused duration ~12 minutes unclear

Example Movement along shape Measurement

3 5 Not focusedOther

Cluster IndexD

ura

tio

n

100000

10000

1000

100

10

1

01

Po

sitio

n


Relevance of Cluster Analysis (Black box)

Production breaks and tool exchange well recognized

Adjustments and others yet not distinguished

Cluster 4 points out measurements (unexpected result)

Detailed analysis for cluster 4 4h Measurement 8h Adjustments

Plausible segmentation of relevant standstill times by clustering

Profile White box Cluster Black box

Production 377 Days 2 359 Days

Breaks 293 Days 1 299 Days

Adjustments 87 h 018 h

Tool exchange 114 h 6 114 h

Measurements - 4 1236h

Other 141 h 3 5 042h

4h8h

100000

10000

1000

100

10

1

01

Cluster Index

Du

ratio

n


Stable Anomaly Detection in Machine Data


Analyze Sensor Data From CNC Machine

Sensor logs from turning machine using multiple tools on one work piece

Many files (one per tool application) with sensor readings (100second)

Find unusual sensor readings pointing to production failure

Reliable automatic detection of complex failures


Start with a Quick Overview

Out-of-the-box histograms

and column statisticsBuild DragampDrop Infographics to discover more details

8017 is the most used tool


Visual Analytics Time Series per lsquoToollsquo

Identify suspicious

graph


Anomaly is Deviation from Average

Group movements by lsquotoolrsquo

calculate distance to average path

Most movements

close to averageHigh distance

of outliers

Same tool used

in two modes


Refine Grouping to Avoid Artefacts

lsquoToolrsquo is good grouping criterion but sometimes too coarse

To find supplementary criteria becomes costly

Brute force clustering is easy (all observable parameters)

Combination of tool and cluster delivers stable grouping

hellip distinguishes the different

working modes (without manual

model refinement)


Summary

Challenge Process and data variety

Approach Machine learning artificial intelligence

Examples Application of k-means Effective standstill analysis

Stable anomaly detection

Outlook ML AI very promising for big data analysis

Combination with Big Data classical analytics

Fast time to value by integrated Big Data solution

More examples in the exhibition area


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Artificial Intelligence for Industry 40

Dr Fritz Schinkel

Head of Competence Center Big Data

Fujitsu



Big Data

CollectStream


Devicessensors


Model LearnAnalyze

FindDecideNavigate



production






Individualized

Production

Lots of

different

models

Variety of

processes

Machine

learning



Data

Computer

Result

Data

Computer

Result


data



ee p learn ing

Neuroscience

Machine learning


- Image recognition

- Voice recognition

- Emotionstate

recognition

- Natural-language

processing


amp discovery

- Pattern discovery

- Inference amp

Planning

- Prediction amp

optimization

- Interactivity amp

recommendation

Human Centric AI


Learning

AdvancedResearch





recognition



- Pattern discovery


recommendation







=

Stop

when no

changes

Color like

closest cross

Move crosses

to centers of

groups

Position k

colored

crossesIterate









Total Calendar Time



Running Time

Net Operation Time


loss

Perform

loss

Availability

loss

Planned

downtime

Main-

tenance

No

material

Adjust-

ments

Tool

exchange

UpdatesMicro

stoppage

Break-

down

Breaks

weekends



White box model




Black box model







Precise evaluation

Low compute effort









Calculation

time

Break

Adjustment

Tool exchange

Prog

ram

sta

tus 119872119894119888119903119900 119904119905119900119901119901119886119892119890 = 119905119904119905119886119903119905

119905119890119899119889 120594119904119905119886119905119906119904_6(119905)119889119905



120594119904119905119886119905119906119904_6 119905 = 1 119891119900119903 119904119905119886119905119906119904 119905 = 60 119891119900119903 119904119905119886119905119906119904(119905) ne 6


N =119905119890119899119889minus119905119904119905119886119903119905

001119904



Observable position

Duration


Clear segmentation


Identify expected

outcome

Learnings from the

unexpected

Interpretation

Silhouettes

00 05 08

Between -1 and 1

High is goodSta

ndstill

clu

ste

rs


Dura

tion

100000

10000

1000

100

10

1

01









Cluster IndexD

ura

tio

n

100000

10000

1000

100

10

1

01

Po

sitio

n














Other 141 h 3 5 042h

4h8h

100000

10000

1000

100

10

1

01

Cluster Index

Du

ratio

n
















Identify suspicious

graph





Most movements


of outliers

Same tool used

in two modes









model refinement)


Summary
























Big Data

CollectStream


Devicessensors


Model LearnAnalyze

FindDecideNavigate



production






Individualized

Production

Lots of

different

models

Variety of

processes

Machine

learning



Data

Computer

Result

Data

Computer

Result


data



ee p learn ing

Neuroscience

Machine learning


- Image recognition

- Voice recognition

- Emotionstate

recognition

- Natural-language

processing


amp discovery

- Pattern discovery

- Inference amp

Planning

- Prediction amp

optimization

- Interactivity amp

recommendation

Human Centric AI


Learning

AdvancedResearch





recognition



- Pattern discovery


recommendation







=

Stop

when no

changes

Color like

closest cross

Move crosses

to centers of

groups

Position k

colored

crossesIterate









Total Calendar Time



Running Time

Net Operation Time


loss

Perform

loss

Availability

loss

Planned

downtime

Main-

tenance

No

material

Adjust-

ments

Tool

exchange

UpdatesMicro

stoppage

Break-

down

Breaks

weekends



White box model




Black box model







Precise evaluation

Low compute effort









Calculation

time

Break

Adjustment

Tool exchange

Prog

ram

sta

tus 119872119894119888119903119900 119904119905119900119901119901119886119892119890 = 119905119904119905119886119903119905

119905119890119899119889 120594119904119905119886119905119906119904_6(119905)119889119905



120594119904119905119886119905119906119904_6 119905 = 1 119891119900119903 119904119905119886119905119906119904 119905 = 60 119891119900119903 119904119905119886119905119906119904(119905) ne 6


N =119905119890119899119889minus119905119904119905119886119903119905

001119904



Observable position

Duration


Clear segmentation


Identify expected

outcome

Learnings from the

unexpected

Interpretation

Silhouettes

00 05 08

Between -1 and 1

High is goodSta

ndstill

clu

ste

rs


Dura

tion

100000

10000

1000

100

10

1

01









Cluster IndexD

ura

tio

n

100000

10000

1000

100

10

1

01

Po

sitio

n














Other 141 h 3 5 042h

4h8h

100000

10000

1000

100

10

1

01

Cluster Index

Du

ratio

n
















Identify suspicious

graph





Most movements


of outliers

Same tool used

in two modes









model refinement)


Summary
























Individualized

Production

Lots of

different

models

Variety of

processes

Machine

learning



Data

Computer

Result

Data

Computer

Result


data



ee p learn ing

Neuroscience

Machine learning


- Image recognition

- Voice recognition

- Emotionstate

recognition

- Natural-language

processing


amp discovery

- Pattern discovery

- Inference amp

Planning

- Prediction amp

optimization

- Interactivity amp

recommendation

Human Centric AI


Learning

AdvancedResearch





recognition



- Pattern discovery


recommendation







=

Stop

when no

changes

Color like

closest cross

Move crosses

to centers of

groups

Position k

colored

crossesIterate









Total Calendar Time



Running Time

Net Operation Time


loss

Perform

loss

Availability

loss

Planned

downtime

Main-

tenance

No

material

Adjust-

ments

Tool

exchange

UpdatesMicro

stoppage

Break-

down

Breaks

weekends



White box model




Black box model







Precise evaluation

Low compute effort









Calculation

time

Break

Adjustment

Tool exchange

Prog

ram

sta

tus 119872119894119888119903119900 119904119905119900119901119901119886119892119890 = 119905119904119905119886119903119905

119905119890119899119889 120594119904119905119886119905119906119904_6(119905)119889119905



120594119904119905119886119905119906119904_6 119905 = 1 119891119900119903 119904119905119886119905119906119904 119905 = 60 119891119900119903 119904119905119886119905119906119904(119905) ne 6


N =119905119890119899119889minus119905119904119905119886119903119905

001119904



Observable position

Duration


Clear segmentation


Identify expected

outcome

Learnings from the

unexpected

Interpretation

Silhouettes

00 05 08

Between -1 and 1

High is goodSta

ndstill

clu

ste

rs


Dura

tion

100000

10000

1000

100

10

1

01









Cluster IndexD

ura

tio

n

100000

10000

1000

100

10

1

01

Po

sitio

n














Other 141 h 3 5 042h

4h8h

100000

10000

1000

100

10

1

01

Cluster Index

Du

ratio

n
















Identify suspicious

graph





Most movements


of outliers

Same tool used

in two modes









model refinement)


Summary
























Data

Computer

Result

Data

Computer

Result


data



ee p learn ing

Neuroscience

Machine learning


- Image recognition

- Voice recognition

- Emotionstate

recognition

- Natural-language

processing


amp discovery

- Pattern discovery

- Inference amp

Planning

- Prediction amp

optimization

- Interactivity amp

recommendation

Human Centric AI


Learning

AdvancedResearch





recognition



- Pattern discovery


recommendation







=

Stop

when no

changes

Color like

closest cross

Move crosses

to centers of

groups

Position k

colored

crossesIterate









Total Calendar Time



Running Time

Net Operation Time


loss

Perform

loss

Availability

loss

Planned

downtime

Main-

tenance

No

material

Adjust-

ments

Tool

exchange

UpdatesMicro

stoppage

Break-

down

Breaks

weekends



White box model




Black box model







Precise evaluation

Low compute effort









Calculation

time

Break

Adjustment

Tool exchange

Prog

ram

sta

tus 119872119894119888119903119900 119904119905119900119901119901119886119892119890 = 119905119904119905119886119903119905

119905119890119899119889 120594119904119905119886119905119906119904_6(119905)119889119905



120594119904119905119886119905119906119904_6 119905 = 1 119891119900119903 119904119905119886119905119906119904 119905 = 60 119891119900119903 119904119905119886119905119906119904(119905) ne 6


N =119905119890119899119889minus119905119904119905119886119903119905

001119904



Observable position

Duration


Clear segmentation


Identify expected

outcome

Learnings from the

unexpected

Interpretation

Silhouettes

00 05 08

Between -1 and 1

High is goodSta

ndstill

clu

ste

rs


Dura

tion

100000

10000

1000

100

10

1

01









Cluster IndexD

ura

tio

n

100000

10000

1000

100

10

1

01

Po

sitio

n














Other 141 h 3 5 042h

4h8h

100000

10000

1000

100

10

1

01

Cluster Index

Du

ratio

n
















Identify suspicious

graph





Most movements


of outliers

Same tool used

in two modes









model refinement)


Summary
























ee p learn ing

Neuroscience

Machine learning


- Image recognition

- Voice recognition

- Emotionstate

recognition

- Natural-language

processing


amp discovery

- Pattern discovery

- Inference amp

Planning

- Prediction amp

optimization

- Interactivity amp

recommendation

Human Centric AI


Learning

AdvancedResearch





recognition



- Pattern discovery


recommendation







=

Stop

when no

changes

Color like

closest cross

Move crosses

to centers of

groups

Position k

colored

crossesIterate









Total Calendar Time



Running Time

Net Operation Time


loss

Perform

loss

Availability

loss

Planned

downtime

Main-

tenance

No

material

Adjust-

ments

Tool

exchange

UpdatesMicro

stoppage

Break-

down

Breaks

weekends



White box model




Black box model







Precise evaluation

Low compute effort









Calculation

time

Break

Adjustment

Tool exchange

Prog

ram

sta

tus 119872119894119888119903119900 119904119905119900119901119901119886119892119890 = 119905119904119905119886119903119905

119905119890119899119889 120594119904119905119886119905119906119904_6(119905)119889119905



120594119904119905119886119905119906119904_6 119905 = 1 119891119900119903 119904119905119886119905119906119904 119905 = 60 119891119900119903 119904119905119886119905119906119904(119905) ne 6


N =119905119890119899119889minus119905119904119905119886119903119905

001119904



Observable position

Duration


Clear segmentation


Identify expected

outcome

Learnings from the

unexpected

Interpretation

Silhouettes

00 05 08

Between -1 and 1

High is goodSta

ndstill

clu

ste

rs


Dura

tion

100000

10000

1000

100

10

1

01









Cluster IndexD

ura

tio

n

100000

10000

1000

100

10

1

01

Po

sitio

n














Other 141 h 3 5 042h

4h8h

100000

10000

1000

100

10

1

01

Cluster Index

Du

ratio

n
















Identify suspicious

graph





Most movements


of outliers

Same tool used

in two modes









model refinement)


Summary



























=

Stop

when no

changes

Color like

closest cross

Move crosses

to centers of

groups

Position k

colored

crossesIterate









Total Calendar Time



Running Time

Net Operation Time


loss

Perform

loss

Availability

loss

Planned

downtime

Main-

tenance

No

material

Adjust-

ments

Tool

exchange

UpdatesMicro

stoppage

Break-

down

Breaks

weekends



White box model




Black box model







Precise evaluation

Low compute effort









Calculation

time

Break

Adjustment

Tool exchange

Prog

ram

sta

tus 119872119894119888119903119900 119904119905119900119901119901119886119892119890 = 119905119904119905119886119903119905

119905119890119899119889 120594119904119905119886119905119906119904_6(119905)119889119905



120594119904119905119886119905119906119904_6 119905 = 1 119891119900119903 119904119905119886119905119906119904 119905 = 60 119891119900119903 119904119905119886119905119906119904(119905) ne 6


N =119905119890119899119889minus119905119904119905119886119903119905

001119904



Observable position

Duration


Clear segmentation


Identify expected

outcome

Learnings from the

unexpected

Interpretation

Silhouettes

00 05 08

Between -1 and 1

High is goodSta

ndstill

clu

ste

rs


Dura

tion

100000

10000

1000

100

10

1

01









Cluster IndexD

ura

tio

n

100000

10000

1000

100

10

1

01

Po

sitio

n














Other 141 h 3 5 042h

4h8h

100000

10000

1000

100

10

1

01

Cluster Index

Du

ratio

n
















Identify suspicious

graph





Most movements


of outliers

Same tool used

in two modes









model refinement)


Summary






























Total Calendar Time



Running Time

Net Operation Time


loss

Perform

loss

Availability

loss

Planned

downtime

Main-

tenance

No

material

Adjust-

ments

Tool

exchange

UpdatesMicro

stoppage

Break-

down

Breaks

weekends



White box model




Black box model







Precise evaluation

Low compute effort









Calculation

time

Break

Adjustment

Tool exchange

Prog

ram

sta

tus 119872119894119888119903119900 119904119905119900119901119901119886119892119890 = 119905119904119905119886119903119905

119905119890119899119889 120594119904119905119886119905119906119904_6(119905)119889119905



120594119904119905119886119905119906119904_6 119905 = 1 119891119900119903 119904119905119886119905119906119904 119905 = 60 119891119900119903 119904119905119886119905119906119904(119905) ne 6


N =119905119890119899119889minus119905119904119905119886119903119905

001119904



Observable position

Duration


Clear segmentation


Identify expected

outcome

Learnings from the

unexpected

Interpretation

Silhouettes

00 05 08

Between -1 and 1

High is goodSta

ndstill

clu

ste

rs


Dura

tion

100000

10000

1000

100

10

1

01









Cluster IndexD

ura

tio

n

100000

10000

1000

100

10

1

01

Po

sitio

n














Other 141 h 3 5 042h

4h8h

100000

10000

1000

100

10

1

01

Cluster Index

Du

ratio

n
















Identify suspicious

graph





Most movements


of outliers

Same tool used

in two modes









model refinement)


Summary
























White box model




Black box model







Precise evaluation

Low compute effort









Calculation

time

Break

Adjustment

Tool exchange

Prog

ram

sta

tus 119872119894119888119903119900 119904119905119900119901119901119886119892119890 = 119905119904119905119886119903119905

119905119890119899119889 120594119904119905119886119905119906119904_6(119905)119889119905



120594119904119905119886119905119906119904_6 119905 = 1 119891119900119903 119904119905119886119905119906119904 119905 = 60 119891119900119903 119904119905119886119905119906119904(119905) ne 6


N =119905119890119899119889minus119905119904119905119886119903119905

001119904



Observable position

Duration


Clear segmentation


Identify expected

outcome

Learnings from the

unexpected

Interpretation

Silhouettes

00 05 08

Between -1 and 1

High is goodSta

ndstill

clu

ste

rs


Dura

tion

100000

10000

1000

100

10

1

01









Cluster IndexD

ura

tio

n

100000

10000

1000

100

10

1

01

Po

sitio

n














Other 141 h 3 5 042h

4h8h

100000

10000

1000

100

10

1

01

Cluster Index

Du

ratio

n
















Identify suspicious

graph





Most movements


of outliers

Same tool used

in two modes









model refinement)


Summary






























Calculation

time

Break

Adjustment

Tool exchange

Prog

ram

sta

tus 119872119894119888119903119900 119904119905119900119901119901119886119892119890 = 119905119904119905119886119903119905

119905119890119899119889 120594119904119905119886119905119906119904_6(119905)119889119905



120594119904119905119886119905119906119904_6 119905 = 1 119891119900119903 119904119905119886119905119906119904 119905 = 60 119891119900119903 119904119905119886119905119906119904(119905) ne 6


N =119905119890119899119889minus119905119904119905119886119903119905

001119904



Observable position

Duration


Clear segmentation


Identify expected

outcome

Learnings from the

unexpected

Interpretation

Silhouettes

00 05 08

Between -1 and 1

High is goodSta

ndstill

clu

ste

rs


Dura

tion

100000

10000

1000

100

10

1

01









Cluster IndexD

ura

tio

n

100000

10000

1000

100

10

1

01

Po

sitio

n














Other 141 h 3 5 042h

4h8h

100000

10000

1000

100

10

1

01

Cluster Index

Du

ratio

n
















Identify suspicious

graph





Most movements


of outliers

Same tool used

in two modes









model refinement)


Summary
























Observable position

Duration


Clear segmentation


Identify expected

outcome

Learnings from the

unexpected

Interpretation

Silhouettes

00 05 08

Between -1 and 1

High is goodSta

ndstill

clu

ste

rs


Dura

tion

100000

10000

1000

100

10

1

01









Cluster IndexD

ura

tio

n

100000

10000

1000

100

10

1

01

Po

sitio

n














Other 141 h 3 5 042h

4h8h

100000

10000

1000

100

10

1

01

Cluster Index

Du

ratio

n
















Identify suspicious

graph





Most movements


of outliers

Same tool used

in two modes









model refinement)


Summary






























Cluster IndexD

ura

tio

n

100000

10000

1000

100

10

1

01

Po

sitio

n














Other 141 h 3 5 042h

4h8h

100000

10000

1000

100

10

1

01

Cluster Index

Du

ratio

n
















Identify suspicious

graph





Most movements


of outliers

Same tool used

in two modes









model refinement)


Summary



































Other 141 h 3 5 042h

4h8h

100000

10000

1000

100

10

1

01

Cluster Index

Du

ratio

n
















Identify suspicious

graph





Most movements


of outliers

Same tool used

in two modes









model refinement)


Summary





































Identify suspicious

graph





Most movements


of outliers

Same tool used

in two modes









model refinement)


Summary


























Most movements


of outliers

Same tool used

in two modes









model refinement)


Summary






























model refinement)


Summary























Summary






















Artificial Intelligence for Industry 4.0

Technology

Transcript of Artificial Intelligence for Industry 4.0