ict meets mecatech - data mining et big data par pepite

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Page 1: ICT meets MecaTech - Data mining et Big Data par Pepite

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Page 2: ICT meets MecaTech - Data mining et Big Data par Pepite

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Big Data and Data Mining

Philipe Mack

[email protected]

December 1st 2014

Page 3: ICT meets MecaTech - Data mining et Big Data par Pepite

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PRESENTATION

• Pepite SA (www.pepite.be), founded in 2002 to provide

predictive analytics applications in industry

• Product quality (off-spec reduction)

• Operational performance (utilities and raw materials efficiency)

• Maintenance performance (avoidance of excessive degradation of

assets)

• 2 main assets :

• DATAmaestro :

» cloud based data mining software

» provide the most advanced data mining technologies

» designed for users that are not data scientists

» based on 20+ years of research at the Machine Learning Laboratory at

the University of Liege, Belgium

• ENERGYmaestro

» an energy performance management solution

» based on DATAmaestro

» change management and continuous improvement

techniques

Introducing

Basis Weight: 45.0 lb PPS Smoothness: 1.20 µm Brightness: 74 % Color b*: 2.5 Gloss: 53 % Caliper: 58 µm Opacity: 94 %

Page 4: ICT meets MecaTech - Data mining et Big Data par Pepite

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THE BIG DATA DEFINITIONS…

Page 5: ICT meets MecaTech - Data mining et Big Data par Pepite

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BIG DATA IN PRACTICE

Velocity

Variety

Volume

“BIG” qualifier changes with time

“BIG” qualifier changes with application

Page 6: ICT meets MecaTech - Data mining et Big Data par Pepite

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WHY SO MUCH DATA ?

$0.01

$0.10

$1.00

$10.00

$100.00

$1000.00

$10000.00

$100000.00

$1000000.00

1975 1980 1985 1990 1995 2000 2005 2010 2015

Co

st (

$/G

B)

Year

Yearly trend of storage cost

Cost/MB

Year

Sto

rag

e c

osts

($

/G

b)

1E-01

1E+00

1E+01

1E+02

1E+03

1E+04

1E+05

1E+06

1E+07

1E+08

1E+09

1E+10

1E+11

1E+12

1E+13

1950 1960 1970 1980 1990 2000 2010 2020

Cos

t pe

r G

igaF

lops

(in

USD

)

Year Year

Co

st

per G

flo

ps

(in

$)

Page 7: ICT meets MecaTech - Data mining et Big Data par Pepite

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WHAT MEANS BIG DATA IN A PLANT ?

Laboratory

Information

Management

Systems Enterprise

Resources

Planning

Distributed

Control

System

Supervisory

Control And

Data

Acquisition

Computerized

Maintenance

Management

Systems

Historian

BUT still very difficult to have a consistent and holistic view of plant operational performance !

Manufacturing

Execution

Systems

Energy

Management

System

Page 8: ICT meets MecaTech - Data mining et Big Data par Pepite

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THE ANALYTICS CONTINUUM

Source : GARTNER

Page 9: ICT meets MecaTech - Data mining et Big Data par Pepite

Slide | 9Slide | 9Source : McKinsey

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EXAMPLE OF VALUE EXTRACTED FROM « BIG DATA »

SOURCE: Electricity Consumers Resource Council estimated the cost of August 213 blackout in US between $4.5 and $8.2 billions

Predict and understand root causes of breaks in paper sheets

Collect data from hatcheries and provides analytics features to decrease malformation rates

Use historical data to predict real-time steel quality

Increase yield and reduce scrap by 5%

Paper making

Chemicals

Steel making

Hatcheries

Type of project Impact

Forecast dynamic security of transmission grid

Avoid costly curtailment of loads or generations; in the worst case avoid black-outs (several billions $)

Predictive Maintenance project to enhance O&M services

Reduced unplanned down timeCost saving of 10% (lower insurance costs)

Wind mills

Electrical network

Analyze drilling operation data to increase ROP

Faster drilling and less downtimes due to reduced well head failureE&P drilling

operations

Optimize use of energy in exothermic processes

Reduce shutdowns and increases OEE by 5%

Reduce energy costs by 15%

Reduce malformation rates of fish by 20%

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PREDICTIVE MAINTENANCE

Page 12: ICT meets MecaTech - Data mining et Big Data par Pepite

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BIG DATA ANALYTICS FOR WINDTURBINES

• How to build the monitoring system ?

• Based on a first “good” set of historical data and FMEA analysis, we

can build and calibrate the smart agents

• DATAmaestro data mining solution screens historical data set to:

• Discover relevant relationships between variables (tags) and records (data)

in wind turbine historical data via explorative analyses: dendrogram,

clustering tools, advanced predictive tools like a decision tree

dendogram: to describe the

dependencies among variablesdecision tree: discovers best

operating conditions

Page 13: ICT meets MecaTech - Data mining et Big Data par Pepite

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MONITORING

Historian DB CMMS DB

Smart Agents

Smart agents are scanning continuously incoming data

Failure pattern detected

Alarm Work Order

System Reconfiguration

CMMS updated

New failure pattern ? New Smart Agents

New Normal operation conditions ?

Smart Agents updated

Web Interface

- Machines health information

- Alarms- Planning- Online Reporting

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SCREENSHOT OF APPLICATION

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PERFORMANCE ANALYTICS

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ASU is divide into two separation columns :

- HP column

- LP column

Data collected are located on the LP part of the

process.

AIR SEPARATION UNIT

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SPECIFIC ENERGY CONS. (KWH/T O2)

KWh/T

Date

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WHAT EXPLAIN THE VARIABILITY OF

ENERGY EFFICIENCY

Automatic Pareto analysis (1) and

decision tree (2) helps us to diagnose

the drift and understand which and how

parameters explain the drift.

Obvioiusly T° plays a strong role in the

model drift => we need to include it as

an input in the model; we cannot

change the T° !

1 2

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KWH/T PREDICTIVE MODEL V2

By including the T° we are much

better to predict the KWh/T

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CONCLUSIONS

• Big data combined with predictive analytics can help to

improve performance and maintenance of production

assets

• Proven approach to support lean program or any other

performance management program

• Data collection/quality remains a major roadblock in

industrial applications

• Still a lack of understanding of what is big data and

analytics

• Still a big gap between data scientists and business

people

• Always think about the business value! KISS and 80/20

rules…