1. Proactive Fault Management by Big Data Usage Data Science: Theory and Application RWTH Aachen University - ICT cubes, October 26, 2015 Gregor Fuhs M.Sc. RWTH © FIR 2015 Gregor Fuhs, FIR an der RWTH AachenRWTH Aachen University | 26.10.2015

2. © FIR 2015 Gregor Fuhs, FIR an der RWTH AachenBitkom AK Big Data | 25.09.2015 Motivation and Basics © FIR 2015 Gregor Fuhs, FIR an der RWTH AachenRWTH Aachen University | 26.10.2015

3. So, why do you do it with your data? You never would do that! © FIR 2015 Gregor Fuhs, FIR an der RWTH AachenBitkom AK Big Data | 25.09.2015© FIR 2015 Gregor Fuhs, FIR an der RWTH AachenRWTH Aachen University | 26.10.2015

4. Traffic Data GPS- Data Data is generated everywhere – Why shouldn’t we use it? Smartphone Data Camera Data Customer Data © FIR 2015 Gregor Fuhs, FIR an der RWTH AachenRWTH Aachen University | 26.10.2015

5. Big Data and Smart Data Ansatz © FIR 2015 Gregor Fuhs, FIR an der RWTH AachenRWTH Aachen University | 26.10.2015

6. Size VolumeVelocityVariety Veracity Speed Structuredness defective Data fast Data Generation Different Formats Complexity Machine Learning Cyber Physical Systems Cloud Web online Sensor Data Google IBM Facebook Twitter Smartphone Unstructuredness GPS RFID Hypervisor Web Server E-Mail Messaging Clickstreams Databases Sensory Telematics Security Devices Map Reduce Hadoop Real Time Analysis NSA SETI LHC NASA NCCS DNA Amazon Internet of Things Structure Petabyte Zettabyte Information Search Science Support Governance Management Logs „640 kB ought to be enough for anybody. “ 1981, Bill Gates (disclaimed) Bill Gates for Geoengineering Microsoft Let's talk about cloud ships BIG DATA © FIR 2015 Gregor Fuhs, FIR an der RWTH AachenRWTH Aachen University | 26.10.2015

7. What is Big Data? Data Generation Where comes the data from? Data Storage How is Data stored? Data Analysis How to analyze Big Data? © FIR 2015 Gregor Fuhs, FIR an der RWTH AachenRWTH Aachen University | 26.10.2015

8. How differs Smart Data? Data Generation Where comes the Data from? Data Storage How is Data stored? Data Analysis How to analyze Big Data? Data Generation Data Storage Data is stored by Categories Data Analysis Only relevant Data is analyzed Data Categorization Data is categorized by certain Criteria © FIR 2015 Gregor Fuhs, FIR an der RWTH AachenRWTH Aachen University | 26.10.2015

9. Application of Big Data Methods in BigPro © FIR 2015 Gregor Fuhs, FIR an der RWTH AachenRWTH Aachen University | 26.10.2015

10.  Fault Management is reactive or predictive  Employees are unconsidered as Fault Indicator  Reactive fault Management is cost intensive Status Quo Fault Management  Amount of Data exceeds Processing Capacity  Different Data Sources acerbates unified Treatment in Business Context  Data Processing is not in Real Time, Decisions are based on outdated Data/Information  Unused Data is wasted entrepreneurial Potenzial Status Quo Data Management   Research Project BigPro accesses practical problems of Data- and Fault Management © FIR 2015 Gregor Fuhs, FIR an der RWTH AachenRWTH Aachen University | 26.10.2015

11. 11 The work packages are aligned with the target picture of the project Data generation Data analysis/ -monitoring Pattern recognition/ prognosis Counter measures Real-time Big Data-Platform for failure management Data from production, the environment and from staff User-oriented Visualization Mood-Monitoring Human meta data Machine-/ Process-Sensors Complex Online Optimization Data linking CEP Measurements generation Scalable Visualization Pattern-Management Data generation Data aggregation Information flow material Sentiment- Analysis WP 1 Requirements analyzis WP 2 Design / realization Data platform WP 5 Scalable visualization WP 6 Testing phase DFA Aachen GmbH WP 3 Monitoring and prognosis WP 4 Failure management WP 7 Testing phase C. Grossmann GmbH WP 8 Testing phase Robert Bosch GmbH Picture: own source

12. Reactive Pattern Proactive Pattern Event Producer Event Consumer Historical Data Engine Predictive Pattern Generator Source: FZI Forschungszentrum Informatik 80% in next 10 Minutes Pattern DB Proactive Event-Driven Computing © FIR 2015 Gregor Fuhs, FIR an der RWTH AachenRWTH Aachen University | 26.10.2015

13. RFID-based Tracing 6 weeks Lead Time ERP: TimeLine MDE/BDE © FIR 2015 Gregor Fuhs, FIR an der RWTH AachenRWTH Aachen University | 26.10.2015

14. Facility-Sensors with 10.000 Data Generators MES / MDE / BDE Several hundreds machines, 24/7 operation process, context-, machine data, … © FIR 2015 Gregor Fuhs, FIR an der RWTH AachenRWTH Aachen University | 26.10.2015

15. Assembly e.GO KART Pick-by-Voice commissioning Process deviations © FIR 2015 Gregor Fuhs, FIR an der RWTH AachenRWTH Aachen University | 26.10.2015

16. User specific Warning before Fault occurring Pattern Detection in BigPro Platform Warnings will be directly given to the mechanic or production manager Via „Thinking Aloud“ and RTLS Tracking process times will be recorded and submitted to the system, such that faults can be assigned to the respective process step. Probability of Faults are calculated by the generated Data Patterns Verbal Communication Automatic Process time Tracing Strain Monitoring © FIR 2015 Gregor Fuhs, FIR an der RWTH AachenRWTH Aachen University | 26.10.2015

17. outlook © FIR 2015 Gregor Fuhs, FIR an der RWTH AachenRWTH Aachen University | 26.10.2015

18. Skills Machine Park and Production skills Experience & Knowledge Company Customer Configuration Maintenance Change/Expansion Condition Monitoring Product Live Cycle Preproduction Machine configuration Product configuration Sensor data Serial production Sensor data Defect analysis CIP Production Draft Elaborate Response Data: Improved Visualization and Detection of:  Business skills  Response/Problems in Production  Response from Customer by Digitalisation. Live-Information- Provision: Direct Depiction of Impacts of Changes in Time, Quality and Costs. Planning Product Design Impact Calculator ►Live-Description of Impacts of Decisions ►Task-specific View (reduction on relevant Information) Improvement of Product Design by Big Data and Smart Data Methods Design © FIR 2015 Gregor Fuhs, FIR an der RWTH AachenRWTH Aachen University | 26.10.2015

19. Data Source 1 VISTEK Providing required data infrastructure and connecting datasets DFA Aachen Providing data from production process / environment for analysis Data Storage Layer Data Analysis Layer Production, Sensor and Data Providing Layer Sabanci Developing problem-oriented data algorithms Data Source 2 FIR Process-based data requirements analysis and anonymization Data filter Categorization/ Anonymization Key Data flow Material flow Algorithm 1 Algorithm 2 Data Connection Connected and analyzed Data flow Production step 2 Production Step 1 ProMaQ – Data Anonymization for Outsourcing Data Analysis © FIR 2015 Gregor Fuhs, FIR an der RWTH AachenRWTH Aachen University | 26.10.2015

20. SensorFusion – innovative Combination of measured Data and Sensors for a Predictive Maintenance Scenario Sensor Fusion [K] [Hz] Measurement a Time Measurement parameter c Predictive Reactive Maintenance Machine Data Extra Sensor Easy Sensor Upgrade Plug&Play-Data combination and -aggregation Predictive Maintenance by Pattern Recognition Measurement b Research Objective 1Research Objective 2Research Objective 3 © FIR 2015 Gregor Fuhs, FIR an der RWTH AachenRWTH Aachen University | 26.10.2015

21. Thank you for your attention! © FIR 2015 Gregor Fuhs, FIR an der RWTH AachenRWTH Aachen University | 26.10.2015

22. 22 Die Arbeitspakete des Projekts spannen das Zielbild des Projekts BigPro auf Datenbereit- stellung/ -erhebung Datenanalyse/ -monitoring Muster- erkennung/ Prognose Maßnahmen- bewertung/ Reaktion Echtzeitfähige Big Data-Plattform für Störungsmanagement Produktions-, Umgebungs- und Mitarbeiterdaten Bedarfsgerechte Visualisierung Mood-Monitoring Metadaten Mensch Maschinen-/ Prozess-Sensorik Complex Online Optimization Datenvernetzung CEP Maßnahmen- generierung Skalierbare Visualisierung Pattern-Management Datenentstehung Datenaggregation Informationsfluss über Unternehmensebenen Materialfluss Sentiment- Analyse AP 1 Anforderungsanalyse AP 2 Design / Realisierung Datenplattform AP 5 Skalierbare Visualisierung AP 6 Testphase DFA Aachen GmbH AP 3 Monitoring und Prognose AP 4 Reaktions- management AP 7 Testphase C. Grossmann GmbH AP 8 Testphase Robert Bosch GmbH Bildquelle: eigene Darstellung