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Similar to Big Data, Physics, and the Industrial Internet: How Modeling & Analytics are Making the World Work Better (Matt Denesuk)
Similar to Big Data, Physics, and the Industrial Internet: How Modeling & Analytics are Making the World Work Better (Matt Denesuk) (20)
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Big Data, Physics, and the Industrial Internet: How Modeling & Analytics are Making the World Work Better (Matt Denesuk)
- 1. Big Data, Physics, and the Industrial Internet How Modeling Analytics are Making the World Work Better. Matt Denesuk Chief Data Science Officer GE Software October 2014 Imagination at work. Contact: matthew.denesuk@ge.com © General Electric Company, 2014. All Rights Reserved.
- 2. What’s this all about? Industries that are all about data IT see outsized productivity performance gains • Telecom, financial srvcs,… 2 Making industrials all about data IT will transform how the world works • Power, water, aviation, rail, mining, oil gas, manufacturing, … And Big Data + Physics is the enabler
- 3. What happened when 1B people became connected? Entertainment digitized [ ] © General Electric Company, 2014. All Rights Reserved. Social marketing emerged Communications mobilized IT architecture virtualized Retail ad transformed Consumer Internet [ ] [ ] [ ] [ ]
- 4. Now what happens when 50B Machines become connected? Industrial Internet Brilliant Power Logistics Optimization Brilliant Factory Factory Optimization Smart Grid Hospital Optimization Real-time Network Planning Intelligent Medical Devices Connected Machines Brilliant Hospital Brilliant Rail Yard Shipment Visibility Employees increase OT is virtualized Analytics become predictive productivity Machines are self healing automated Monitoring and maintenance is mobilized [ [ © General Electric Company, 2014. All Rights Reserved.
- 5. Cornerstone of IoT Transformation is Software-Defined Machines (SDM’s) CONSUMER COMMERCIAL INDUSTRIAL • Easily connect machines to Internet • Embed apps and analytics into machines and cloud, making them intelligent and self-aware • Change and update capabilities of machines and devices without changing hardware • Deliver intelligence to users providing continuously better outcomes • Extend Industrial Internet platform via API and ecosystem
- 6. Example: Wind Farm in Analytics Age (40 TB/yr/ 500 wm farm)
- 7. The Value to Customers is Huge Efficiency and cost savings, new customer services, risk avoidance – 1% improvements cuts $276B in waste across industries Industry Segment Type of savings $30B 7 GESoftware.com | @GESoftware | #IndustrialInternet Aviation Power Healthcare Rail Oil and Gas Estimated value over 15 years $66B $63B $27B $90B Commercial Gas-fired generation System-wide Freight 1% fuel savings Exploration and development 1% fuel savings 1% reduction in system inefficiency 1% reduction in system inefficiency 1% reduction in capital expenditures Note: Illustrative examples based on potential one percent savings applied across specific global industry sectors. Source: GE estimates
- 8. 4 Big Data Forces shaping the Industrial Internet 8 GESoftware.com | @GESoftware | #IndustrialInternet Internet 1 of things Intelligent, SW-defined machines 2 Big Data Analytics 3 Physics + A living network of machines, data, and people Increasing system intelligence through embedded software Employing deep physics engineering models to leap-frog what’s possible with data-driven techniques Transforming massive amounts of data into intelligence, generating data-driven insights, and enhancing asset performance
- 9. Reference Architecture Platform for the Industrial Internet must bridge OT IT Single Record of Asset Business Process Management Industrial Big Data Management Event Processing PaaS SaaS Industrial Data Lake Analytics Modeling Integration with ERP / CRM Device mgmt. M2M, M2H, M2C Insight to Action • Maintenance • SW Upgrades • Machine Control Mobility and Collaboration Cyber-Security Operational Reliability Any Machine Any Device
- 10. What do we need from Data Science? 10
- 11. 11 Two ways of seeing a data set* (and the world) Computer Scientist: “get the knowledge locked in the data” The data set is record of everything that happened, e.g., • All customer transactions last month • All friendship links between members of social networking site Goal is to find interesting patterns, rules, and/or associations. Physical Scientist – “get the knowledge” (*See D. Lambert, or R. Mahoney, e.g.) • The data set is an partial, and often very noisy reflection of some underlying phenomenon, e.g., – Emission spectra from stars – Battery voltage varying with current, time, and temperature • Goal is better understanding or ability to predict aspects of that phenomenon, often through a mathematical model For certain kinds of problems, immense power in the combination
- 12. Example: Statistical Translation • Employ language experts to codify rules, exceptions, vocabulary mappings, etc. • Apply transformation to user’s query. • Gather and classify lots of translated docs (websites, UN, books, …) • Identify match patterns • Map to user’s translation query. Regular Science approach Statistical (data-driven) approach Use of language is infinitely complex, but you can teach a computer all the rules and content. People say the same kind of things over and over. And somebody has already translated it. • Costly, hard to scale • Can translate nearly any statement (but accuracy variable) • In theory, could be better than human. • Incrementally low cost, highly scalable. • Limited in scope to digitized docs that have been translated before • Limited by skill of human translators Will flop with innovative use of language (new poetry, …) Too expensive and difficult to deploy comprehensively
- 13. 13 Three basic components of Industrial Data Science Physics/engineering-based models • Need much less data • Powerful, but difficult to maintain and scale Empirical, heuristic rules insights • Straightforward to understand • Captures accumulated knowledge of your experts Data-driven techniques – machine learning, statistics, optimization, advanced visualization, … • Often not enough data in the industrial domain • Bias: limited to regions of parameter space traversed in normal operation • But easiest to maintain and scale
- 14. 14 © 2014 General Electric Company - All rights reserved Some Patterns
- 15. 15 Industrial Example: improving rule based systems Many equipment operators have a system something like this, with rules derived based on experience and intuition. Rule sets implemented in Analytics Engine Produce alerts Low-latency operational data Alerts
- 16. 16 Industrial Example: improving rule based systems Rule sets implemented in Analytics Engine Produce alerts Low-latency operational data Pattern, sequence, association mining, etc. Outcome data Combine ML plus rule-based alerts with outcome data to produce better alerts More actionable alerts
- 17. 17 Industrial Example: improving rule based systems Rule sets implemented in Analytics Engine Low-latency operational data Outcome data Recommendation engine Use ML and outcome data to refine and extend rule base, providing yet further actionability, resulting in substantial improvements in operational outcomes. Tune parameters of existing rules, and create new rules. Actionable Recommendations
- 18. 18 Another Industrial Example: use advanced physical models to create new features for ML approaches Sensor Data Predicted Values and Δs Variety of Machine Learning Techniques Outcome data Using as ML features the: 1. Deviations from expected physics, 2. Inferred or hidden parameter estimates provides much richer and effectively less noisy data, resulting in much stronger predictions and models.
- 19. Fleet/operation-wide optimization levels. Trade-offs to optimize business performance 19 Climbing up the value chain toward Condition-based Performance Management and Business Optimization. Need: • Earlier detection • Root cause • Scaling to more equipment Types instances 19 Fix it when it breaks Prescriptive recommendations (multi-channel) Predictive Maintenance (“future”) Condition-based Maintenance (“now”) Model-driven Work-driven Time-driven New levers for optimization across the operation or business “Equipment heath is not a given, but a variable”
- 20. 20 Capability / Impact Ramp Sophisticated, optimized management of business Complexity Science Predictive analytics Rules Data Anomaly augmentation Detection Advanced Basic Reporting Reporting Data completeness, breadth, quality Operational optimization Prescriptive analytics Alerts Highly-actionable management info High-value guidance operations
- 21. Broad range of deep Data Science capabilities needed Optimizes the design operations of complex business and physical systems, extracting more value at lower risk Innovates new ways of performing reliability analysis, statistical modeling of large data, biomarker discovery and financial risk management Focuses on developing algorithms and systems for real time video analysis Research in algorithms and software systems that analyze understand images to produce actionable insights Develop scalable and cross-disciplinary machine learning predictive capabilities to derive actionable insights from big data Modeling complex system and noise processes to detect subtle deviations and estimate critical system parameters Industrial Data Science Employing deep physical and engineering understanding of equipment and processes to generate normative models. Sensor Signal Analytics Knowledge Discovery Delivering data and knowledge-driven decision support via semantic technologies and big data systems research Applied Statistics Physics expert-based Modeling Machine Learning Computer Vision Image Analytics Optimization Management Science 21
- 22. 22 “Industrial Data Science” ① Outcome-oriented application of mathematical physics-based analysis models to real-world problems in industrial operations. ② Tools processes needed to do that continually at scale. Improve the performance of industrial operations, e.g., • Higher equipment uptime, utilization, • Lower maintenance/shop costs, longer component life • Fleet level optimization trade-offs • Business optimization (linking to financial customer data) • Service / contract management Combination of : • Physical expert modeling experience depth • Installed base of industrial equipment and data. • Big Data, Machine Learning, and statistical capabilities Industrial Data Science What is it? Why do we do it What’s needed