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EDF2013: Keynote Gerhard Kreß: Big Data in Industrial Applications
 

EDF2013: Keynote Gerhard Kreß: Big Data in Industrial Applications

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Keynote talk of Gerhard Kreß, Director Strategic Transformation at Siemens AG, at the European Data Forum 2013, 10 April 2013 in Dublin, Ireland: Big Data in Industrial Applications

Keynote talk of Gerhard Kreß, Director Strategic Transformation at Siemens AG, at the European Data Forum 2013, 10 April 2013 in Dublin, Ireland: Big Data in Industrial Applications

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    EDF2013: Keynote Gerhard Kreß: Big Data in Industrial Applications EDF2013: Keynote Gerhard Kreß: Big Data in Industrial Applications Presentation Transcript

    • Dublin – April 10, 2013Big Data in industrial applicationsKeynote European Data Forum2013© Siemens AG 2013 All rights reserved. siemens.com/answers
    • Topics of the presentation 1 Siemens as a leading software provider 2 Siemens perspective on Big Data 3 Examples from offerings and research 4 What can we expect from big Data in industrial applications?Page 2 2013-04-10 Siemens AG © Siemens AG 2013 All rights reserved.
    • Siemens is a global company active inIndustry, Energy, Infrastructure and cities andHealthcare Revenue by Sector Revenue by Region Healthcare Germany Europe, CIS, 17% Energy 14% Africa, Middle East 35% Asia, 37% (excl. Germany) Infra- 22% Australia 20% structure 26% 29% Industry Americas Based on customer location Continuing operations – Revenue and employees comparison with previous year Revenue in millions of € Employees in thousands 100,000 500 In millions of € FY 2011 FY 2012 80,000 400 New orders 85,166 76,913 60,000 300 Revenue 73,275 78,296 40,000 200 Income 7,376 5,184 20,000 100 Free cash flow 5,918 4,790 0 Employees 359,000 370,000 FY 1986 1990 1995 2000 2005 2012As reported in annual reportsPage 3 2013-04-10 Siemens AG © Siemens AG 2013 All rights reserved.
    • Siemens aims to being a pioneer in technologydriven markets Future of energy High-performance technologies for the generation, transmission, distribution and use of energy • Highly efficient power generation from fossil fuels as well as renewable sources • Smart grids that integrate decentralized power generation and energy storage units • Comprehensive electromobility solutions – from charging infrastructures to drives Vertical IT Integrated industry-specific hardware and software solutions • Industrial automation • Building automation • Transport logistics • Healthcare IT SMART products for local markets Innovative, robust products for entry-level market segments – developed in local markets for local markets and for customers around the globePage 4 2013-04-10 Siemens AG © Siemens AG 2013 All rights reserved.
    • Vertical IT is a new and fast growing market Horizontal IT Vertical IT Vertical IT – a new market EquipmentPage 5 2013-04-10 Siemens AG © Siemens AG 2013 All rights reserved.
    • Siemens aims for leadership in vertical IT bycombining domain know how and technology Infrastructure Key strengths to Industry Energy Healthcare & Cities leverage: • Deep domain know-how and customer intimacy • Outstanding• PLM • Smart Grid • Plant mgmt • IT workflows technology• Production SW • Smart buildings • Plant automation • Patient record• Computer aided • Intelligent traffic • Renewables management • Global design • E-health presence Vertical IT & Software Horizontal IT (Infrastructure, tools, platforms and services)PLM: Product lifecycle managementPage 6 2013-04-10 Siemens AG © Siemens AG 2013 All rights reserved.
    • Topics of the presentation 1 Siemens as a leading software provider 2 Siemens perspective on Big Data 3 Examples from offerings and research 4 What can we expect from big Data in industrial applications?Page 7 2013-04-10 Siemens AG © Siemens AG 2013 All rights reserved.
    • The future of big data will be in industrial data Social media today mostly in focus In 2000 years, the world generated approximately two Exabytes of FACEBOOK GROWS new information: 250MILLION PHOTOS / DAY 2,000,000,000,000,000,000 Social Media Mobile devices The future will focus more on sensor data It now generates that much data in ONE OIL RIG OFFERS 25 THOUSAND READING METERS EVERY 15 MINS. IS 1 day 3,000X MORE DATA POINTS/SEC DATA INTENSIVE Geophysical Medical Smart Exploration Imaging GridsPage 8 2013-04-10 Siemens AG © Siemens AG 2013 All rights reserved.
    • However, data alone is not sufficient to drivemeaningful actions Sensor data Vertical knowledge ONE OIL RIG OFFERS 25 THOUSAND DATA POINTS/SEC Actions READING METERS EVERY 15 MINS. IS 3,000X MORE DATA INTENSIVEPage 9 2013-04-10 Siemens AG © Siemens AG 2013 All rights reserved.
    • Big Data will transform industrial systemsDimensions of big data Analyze data for Generate analytics • Optimization of complex system complex systems, not answers while they still behavior only components matter • Real time decisions in operational processes • Improvement of sustainability of Provide additional industrial processes data context to the VarietyPage 10 2013-04-10 Siemens AG © Siemens AG 2013 All rights reserved.
    • Topics of the presentation 1 Siemens as a leading software provider 2 Siemens perspective on Big Data 3 Examples from offerings and research 4 What can we expect from big Data in industrial applications?Page 11 2013-04-10 Siemens AG © Siemens AG 2013 All rights reserved.
    • Big Data in the European Industrial SectorsExamples from the Energy SectorEmbracing big data requires both data sharing policies to preserve privacy &confidentiality and scalable data analytics• Intelligent on demand reconfiguration of transmission and distribution networks to accommodate both large renewable energy parks as well as small distributed generation• Implementing flexible tariffs for industrial and private demand side management, distributed feed-in, and e-car roaming Price signals Weather Renew- TSO1 TSO2 Industrial DSM ables parks DSO11 DSO21 DSO12 Sustainable Prosumer e-car roaming connected cities TSO – Transmission System Operator DSO – Distribution System OperatorPage 12 2013-04-10 Siemens AG © Siemens AG 2013 All rights reserved.
    • Data Management and Real Time Monitoringfor Gas Turbines Benefits • Improved turbine ramp-up with less vibrations (lower maintenance needs) • Reduced NOx Emissions Online-Data: ca. 5,000 variables / s • Increase of turbine efficiency Real-time Data Analysis (1,000 Neural Models) • Guiding turbine Modules development process Database: Input data and model results Complete Data and Dependency Analysis plus Learning OptimizationPage 13 2013-04-10 Siemens AG © Siemens AG 2013 All rights reserved.
    • Forecasting Wind Power Supply with NeuralNetworks Benefits Accurate forecasts of the wind energy supply of an entire wind field enable e.g. • The usage of wind power as an instantaneously available energy source, • The disposition of wind power quantities on the spot market • An optimal scheduling of wind turbine maintenance jobs • Efficient power grid management Wind Park StructurePage 14 2013-04-10 Siemens AG © Siemens AG 2013 All rights reserved.
    • Concept for Short Term Solar Power Forecast Benefits • Forecast the solar energy supply of a selected control area up to 15 min • Improve power grid management and balancing of energy mix Solar energy components Forecast Sensing Area Cloud Movement Control Area Cloud CoveragePage 15 2013-04-10 Siemens AG © Siemens AG 2013 All rights reserved.
    • With Power/Plant Monitoring we can detect failures and fatigue in advance Benefits • Detect failures and fatigue in advance • Alert service operators upfront before damage occurs • Mitigate the risk of long term service contracts • Increase the efficiency of remote monitoringCondition monitoring platform that predicts failures by operations• learning from historical data and trends• incorporating it with user defined rules and knowledge Page 16 2013-04-10 Siemens AG © Siemens AG 2013 All rights reserved.
    • Advanced Decision Support for Physicians: Semantic Information links Text and ImagesWhen marking text in reports, associated diagnosis are highlighted in lists and images Page 17 2013-04-10 Siemens AG © Siemens AG 2013 All rights reserved.
    • Topics of the presentation 1 Siemens as a leading software provider 2 Siemens perspective on Big Data 3 Examples from offerings and research 4 What can we expect from big Data in industrial applications?Page 18 2013-04-10 Siemens AG © Siemens AG 2013 All rights reserved.
    • Analytics based on Big Data canhave strong impacts on industry Big Data implications Possible impacts in industry • Optimization of complex system • Optimization of industrial processes behavior across the value chain, including semi-autonomous, self organized • Real time decisions in operational continuous change processes • Reduction in operational risks for • Improvement of sustainability of industrial processes customers • Reduction in capital expenditures • Automation of decision making on the level of complex systems Individual reference cases already implemented – broader scale implementation startedPage 19 2013-04-10 Siemens AG © Siemens AG 2013 All rights reserved.
    • What is required to make this all happen? Continue research on vertical algorithms Implementation of further big data reference cases Capturing the full promise Understanding of big data implications of big data (privacy concerns, risks, etc.) analytics in the industrial context Improvement of analytics skill base in Europe Continued research programs on big data in Europe (basic technologies, standard algorithms, data security and privacy, etc.)Page 20 2013-04-10 Siemens AG © Siemens AG 2013 All rights reserved.