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Artificial Intelligence for Industry 4.0

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The explosion of data is creating huge new demand for analytics. To keep track with this pace, the traditional style of modelling by programming is complemented and sometimes replaced by machine intelligence and automated model learning. In this session, we will share examples from Industry 4.0, leveraging AI technologies for deep insights and enabling economically viable data analysis for business transformation.
Speaker: Fritz Schinkel

Published in: Technology
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Artificial Intelligence for Industry 4.0

  1. 1. 0 © Copyright 2017 FUJITSU Fujitsu Forum 2017 #FujitsuForum
  2. 2. 1 © Copyright 2017 FUJITSU Artificial Intelligence for Industry 4.0 Dr. Fritz Schinkel Head of Competence Center Big Data Fujitsu
  3. 3. 2 © Copyright 2017 FUJITSU Automation of Big Data Value Chain Big Data Collect Stream Structured & unstructured data Devices, sensors, Internet of Things Cleanse / Transform Model / Learn Analyze Find Decide Navigate Research & development, science Operation, automation, production Interactive reporting, advertising Rapid modelling for faster insights Social media, open data, linked data
  4. 4. 3 © Copyright 2017 FUJITSU Challenge for Industry 4.0: Individualized Production  Individualized Analysis Individualized Production Lots of different models Variety of processes Machine learning
  5. 5. 4 © Copyright 2017 FUJITSU Programming vs. Machine Learning Data Computer Result Data Computer Result Program Model Historical data
  6. 6. 5 © Copyright 2017 FUJITSU Zinrai: The Fujitsu Framework for AI ee p learn ing Neuroscience Machine learning Social receptivity Simulation - Image recognition - Voice recognition - Emotion/state recognition - Natural-language processing - Knowledge processing & discovery - Pattern discovery - Inference & Planning - Prediction & optimization - Interactivity & recommendation Human Centric AI Sensing and Recognition Knowledge Processing Decision and support Learning Advanced Research Deep Learning Machine Learning Reinforcement Learning Neuroscience Social Receptivity Simulation People / Businesses / Society ActuationSensing - Image recognition - Voice recognition - Emotion/state recognition - Natural-language processing - Knowledge processing & discovery - Pattern discovery - Inference & Planning - Prediction & optimisation - Interactivity & recommendation Proven through more than 300 AI-related business projects
  7. 7. 6 © Copyright 2017 FUJITSU 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 crosses Iterate
  8. 8. 7 © Copyright 2017 FUJITSU Standstill Analysis in Productions Processes
  9. 9. 8 © Copyright 2017 FUJITSU 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 Updates Micro stoppage Break- down Breaks, weekends
  10. 10. 9 © Copyright 2017 FUJITSU Black & 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
  11. 11. 10 © Copyright 2017 FUJITSU 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 ? Programstatus 𝑀𝑖𝑐𝑟𝑜 𝑠𝑡𝑜𝑝𝑝𝑎𝑔𝑒 = 𝑡 𝑠𝑡𝑎𝑟𝑡 𝑡 𝑒𝑛𝑑 𝜒 𝑠𝑡𝑎𝑡𝑢𝑠_6(𝑡)𝑑𝑡 = 𝑖=0 𝑁−1 𝜒 𝑠𝑡𝑎𝑡𝑢𝑠_6 𝑡 𝑠𝑡𝑎𝑟𝑡 + 𝑖 ∗ 0.01𝑠 ∗ 0.01𝑠 with 𝜒 𝑠𝑡𝑎𝑡𝑢𝑠_6 as characteristic function of times with Status 6 𝜒 𝑠𝑡𝑎𝑡𝑢𝑠_6 𝑡 = 1 𝑓𝑜𝑟 𝑠𝑡𝑎𝑡𝑢𝑠 𝑡 = 6 0 𝑓𝑜𝑟 𝑠𝑡𝑎𝑡𝑢𝑠(𝑡) ≠ 6 and N the number of time intervals for a resolution of 0.01 s N = 𝑡 𝑒𝑛𝑑−𝑡 𝑠𝑡𝑎𝑟𝑡 0,01𝑠
  12. 12. 11 © Copyright 2017 FUJITSU 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 0.0 0.5 0.8 Between -1 and 1, High is good Standstillclusters Costs for k=5,6,7,8,15 Cluster Index Duration 100.000 10.000 1.000 100 10 1 0,1
  13. 13. 12 © Copyright 2017 FUJITSU 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 focused Other Cluster IndexDuration 100.000 10.000 1.000 100 10 1 0,1 Position
  14. 14. 13 © Copyright 2017 FUJITSU 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 3,77 Days #2 3,59 Days Breaks 2,93 Days #1 2,99 Days Adjustments 8,7 h 0,18 h Tool exchange 1,14 h #6 1,14 h Measurements - #4 12,36h Other 1,41 h #3, #5 0,42h 4h8h 100.000 10.000 1.000 100 10 1 0,1 Cluster Index Duration     !
  15. 15. 14 © Copyright 2017 FUJITSU Stable Anomaly Detection in Machine Data
  16. 16. 15 © Copyright 2017 FUJITSU 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 (100/second)  Find unusual sensor readings pointing to production failure  Reliable automatic detection of complex failures
  17. 17. 16 © Copyright 2017 FUJITSU Start with a Quick Overview Out-of-the-box histograms and column statistics Build Drag&Drop Infographics to discover more details 8017 is the most used tool
  18. 18. 17 © Copyright 2017 FUJITSU Visual Analytics: Time Series per ‘Tool‘ Identify suspicious graph
  19. 19. 18 © Copyright 2017 FUJITSU Anomaly is Deviation from Average  Group movements by ‘tool’  calculate distance to average path Most movements close to average High distance of outliers Same tool used in two modes
  20. 20. 19 © Copyright 2017 FUJITSU Refine Grouping to Avoid Artefacts  ‘Tool’ 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 … distinguishes the different working modes (without manual model refinement)
  21. 21. 20 © Copyright 2017 FUJITSU 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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