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ICT meets MecaTech - Data mining et Big Data par Pepite

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ICT meets MecaTech - Data mining et Big Data par Pepite

  1. 1. Slide | 1
  2. 2. Slide | 2 Big Data and Data Mining Philipe Mack ph.mack@pepite.be December 1st 2014
  3. 3. Slide | 3 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 %
  4. 4. Slide | 4 THE BIG DATA DEFINITIONS…
  5. 5. Slide | 5 BIG DATA IN PRACTICE Velocity Volume Variety “BIG” qualifier changes with time “BIG” qualifier changes with application
  6. 6. Slide | 6 WHY SO MUCH DATA ? $1000 000.00 $100 000.00 $10 000.00 $1 000.00 $ 100.00 $ 10.00 $ 1.00 $ 0.10 $ 0.01 Yearly trend of storage cost 1975 1980 1985 1990 1995 2000 2005 2010 2015 Cost ($/GB) Year Cost/MB Year Storage costs ($/Gb) 1E+13 1E+12 1E+11 1E+10 1E+09 1E+08 1E+07 1E+06 1E+05 1E+04 1E+03 1E+02 1E+01 1E+00 1950 1960 1970 1980 1990 2000 2010 2020 1E-01 Cost per GigaFlops (in USD) Year Year Cost per Gflops (in $)
  7. 7. Slide | 7 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 Manufacturing Execution Systems Energy Management System BUT still very difficult to have a consistent and holistic view of plant operational performance !
  8. 8. Slide | 8 THE ANALYTICS CONTINUUM Source : GARTNER
  9. 9. Source : McKinsey Slide | 9
  10. 10. Reduce shutdowns and increases OEE by 5% Reduce malformation rates of fish by 20% Slide | 1100 EXAMPLE OF VALUE EXTRACTED FROM « BIG DATA » Predict and understand root causes of breaks in paper sheets Optimize use of energy in exothermic processes Use historical data to predict real-time steel quality Collect data from hatcheries and provides analytics features to decrease malformation rates Reduce energy costs by 15% SOURCE: Electricity Consumers Resource Council estimated the cost of August 213 blackout in US between $4.5 and $8.2 billions 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 time Cost saving of 10% (lower insurance costs) Wind mills Electrical network Analyze drilling operation data to increase ROP Faster drilling and less downtimes due E&P drilling to reduced well head failure operations
  11. 11. Slide | 1111 PREDICTIVE MAINTENANCE
  12. 12. Slide | 12 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 variables decision tree: discovers best operating conditions
  13. 13. Slide | 13 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
  14. 14. Slide | 14 SCREENSHOT OF APPLICATION
  15. 15. Slide | 1155 PERFORMANCE ANALYTICS
  16. 16. Slide | 16 AIR SEPARATION UNIT ASU is divide into two separation columns : - HP column - LP column Data collected are located on the LP part of the process.
  17. 17. Slide | 17 SPECIFIC ENERGY CONS. (KWH/T O2) KWh/T Date
  18. 18. Slide | 18 WHAT EXPLAIN THE VARIABILITY OF ENERGY EFFICIENCY 1 2 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° !
  19. 19. Slide | 19 KWH/T PREDICTIVE MODEL V2 By including the T° we are much better to predict the KWh/T
  20. 20. Slide | 20 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…

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