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E1: Building the Digital Twin (Predix Transform 2016)


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Understand how to develop analytics models using the Asset and Analytics services within Predix. We'll start with a quick tour of the conceptual framework, and then dive deep into actual modeling and deployment examples that you can use. This session will include demo and code walk-through.

Published in: Technology

E1: Building the Digital Twin (Predix Transform 2016)

  1. 1. E1: Building the Digital Twin on Predix Kevin Yang Principal Software Architect @kevinayang Yan Or Director of Engineering
  2. 2. 2PREDIX TRANSFORM Agenda Digital Twin on Predix Predix Asset & Analytics A Twin in Action Code Walk- through Q&A
  3. 3. 3PREDIX TRANSFORM “GE Renewable recently launched at the Renewable APM Prognostics App, a Predix based application that is powered by GE’s Digital Twin technology built and run on GE Digital’s Predix platform. It uses wind turbine operating, maintenance and inspection data to project future operating conditions and predict turbine component reliability.” – GE Renewable Energy Digital Twin Digital Twins transform Digital Industrial Businesses with high- fidelity, digital replicas of assets to help predict, plan and optimize business outcomes Analytics/Models -Math -Physics -Statistical (machine learning/AI) Operational data Digital Twin technology “Why Digital Threads and Twins Are the Future of Trains” – Jamie Miller, President & CEO at GE Transportation Metadata (structure) Context data
  4. 4. 4PREDIX TRANSFORM Digital Twin Technology Digital Twin Class Digital Twin Instances Created by DT Builders Used in Apps by Developers Analytics Models ML/AI Physics Domain Asset Model Gold Data Connected Digital Twins SR & SE Connectors Asset Data Operational Data Context Data APIs Events Handlers Asset Class
  5. 5. 5PREDIX TRANSFORM Key Enabling Technologies for Digital twin Continuous Data, Low Events Event Data: • Low number of failure events • High cost of events & transactions Discrete Data, High Events Event Data: • 13 M Ad Clicks / day • 5 B Amazon items / year • 7.2 M Apple App downloads / day • 12.7 B Alibaba orders / year Consumer vs. Industrial Internet Per asset model Continuously tuned – new data / insights Scalable – MMs assetsBusiness outcomes Adaptable – new … Domain Data Capabilities Physical + Digital Engineering Model Industrial Analytics Machine Learning & AI Automated Data Pre- Processing Predix Inspection Capabilities Digital Thread Life & Operational Behavior Performance Model Management Model Generation & AutomationKnowledge Extraction 00110 10010 11001
  6. 6. 6PREDIX TRANSFORM Digital Twin Ecosystem Predix builds, executes and manages Digital Twin at Scale Models Model Management Marketplace Data Management Knowledge Management Execution Stewardship Data Sets 00110 10010 11001
  7. 7. 7PREDIX TRANSFORM Rapid Model Building  SDK  Drag-and-Drop  Parallel Training Iterate and Feedback Deploy On Infrastructure: Catalog, Analytics, Data, Asset, Edge, Security, BizOps Fully Integrated with Predix Services Intelligent Industrial Applications Consume Model Execution Monitoring Model Management Build, Run, and Manage
  8. 8. 8PREDIX TRANSFORM Predix Services Security Machine Connectivity Edge Time Series Asset Blob Store SQL Database Data Analytics Catalog Analytics Runtime Analytics Predix UI Mobile Visualization UAA ACS
  10. 10. 10PREDIX TRANSFORM Predix Asset Asset Lifecycle Management Plan Build Deploy Manage Retire Monitoring & Diagnostic Data | Models | Alerts Operation Optimization Op Data | KPIs Knowledge Management Integrated View ERP | CRM | PLM Extensible Asset model, providing “context” of Industrial IOT
  11. 11. 11PREDIX TRANSFORM Asset Features Graph DB – Objects, Relationships Open Model Schema Audit Service Query Engine Scripting Engine – Custom Logic REST APIs Validation & Conformance Common Model Template Service Time Machine System Admin •Extensible •Relationship s •Advanced Query •Fine grain •Event grouping •“What was” •Scripts •Triggers •Sync vs Async •Javascript
  12. 12. 12PREDIX TRANSFORM Predix Analytics Develop Deploy Validate Orchestrate Execute μ μ μ Upload Configure Test Run Manage, operationalize, scale IIOT analytics Model • I/O • Params Model • I/O • Params Model • I/O • Params
  13. 13. 13PREDIX TRANSFORM Analytics Features Web UI Taxonomy Catalog Analytics Artifacts Deploy CloudFoundry Packaging REST APIs Configuration BPMN Orchestration Engine Data Connect Runtime Scheduler Runtime • Java • Python • Matlab • Buildpacks • Security • Sync & Async • Tuning & Scaling • Orchestration • Data handling • Monitoring • Scheduling • Time Series • Asset • SQL • Custom
  17. 17. 17PREDIX TRANSFORM Step-by-step • Define Asset Model • Link Asset to Time Series • Prepare Analytic for Upload • Define I/O Mappings • Define Orchestration • Link Orchestration to Assets
  18. 18. 18PREDIX TRANSFORM Asset Model • Asset Model JSON (Turbines) • Asset/Time Series integration (Tags) { uri: /turbines/abc-123-klm-987, manufacturer: GE Energy, model: 2.3-116, attributes: { nominalPower: { value: 2300, unit: kW }, numBlades: { value: 3 } } { uri: /tags/tuv-456-mno-012, name: Temperature, description: Motor Temperature, tagType: Sensor, sourceKey: 40E503.Temp1 unit: DegC, tagType: /tagTypes/pqr-345-xyz-567, monitoredAsset: /turbines/abc-123-klm-987 } { uri: /tagTypes/pqr-345-xyz-567, name: Temperature, description: Motor Temperature, tagType: Sensor, unit: DegC, } Time Series tag ID
  19. 19. 19PREDIX TRANSFORM Predix Graph Expression Language (GEL) /turbines?filter=attributes.nominalPower=2000..* /tags?filter=(manufacturer=GE Energy)<monitoredAsset /tagTypes?filter=((manufacturer=GE Energy)<monitoredAsset)>tagType Turbine Tag TagType monitoredAsset tagType Turbines with nominal power 2000 or higher Sensor tags on all GE manufactured turbines Sensor tag types on all GE turbines
  20. 20. 20PREDIX TRANSFORM Preparing the Analytic public class MinersRule { public String computeCDM(String jsonStr) throws IOException { Input input = getInput(jsonStr); computeMinersCDM(input.getCurrent_cdm(), input.getRecentStresses(), input.getStressLifeLimits()); Response output = new Response(currentCDM); return writeValueAsString(output); } … { "current_cdm": { "time_stamp": [1,2,3,4], "values": [0.01, 0.03, 0.03, 0.045] }, "recentStresses" : { "time_stamp": [1, 2, 3, 4, 5, 6, 7, 8], "sensor1": [1.0, 2.0, 0.0, 1.0, 2.0, 1.0, 3.0, 1.0], "sensor2": [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0] }, "stressLifeLimits": [100.0, 200.0] } { "updatedCDM”: { "time_stamp": [1,2,3,4,8], "cdm_values": [0.01,0.03,0.03,0.045,0.16] } } 1 2 3 4
  21. 21. 21PREDIX TRANSFORM Orchestration (BPMN) … <process id="MinersRuleOrchestration" isExecutable="true”> <startEvent id="sid-start-event” name="”> <outgoing>sid-miners-rule-in</outgoing> </startEvent> <serviceTask completionQuantity="1" id="sid-miners-rule” isForCompensation="false” name="dfcaf02f-ccaa-420a-9a2f-4f2e0d6e083d::MinersRule::v1" startQuantity="1” activiti:delegateExpression="${javaDelegate}” xmlns:activiti=""> <incoming>sid-miners-rule-in</incoming> <outgoing>sid-end-in</outgoing> </serviceTask> <endEvent id="sid-end-event" name="”> <incoming>sid-end-in</incoming> </endEvent> … Analytic GUID, Name, Version
  22. 22. 22PREDIX TRANSFORM Port-to-Field Map (Time Series) { "analyticName": "MinersRule”, "analyticVersion": "v1”, "iterations": [ { "inputMaps": [ { "valueSourceType": "DATA_CONNECTOR”, "fullyQualifiedPortName": "current_cdm.cdm_values”, "fieldId": "inputCDM”, "queryCriteria": { "start": 0, "end": -1, "tags": [ { "limit": 10, "order": "asc” } ] }, "engUnit": null, "required": true, "dataSourceId": "PredixTimeSeries” }, … … "outputMaps": [ { "fullyQualifiedPortName": "updatedCDM.cdm_values.0”, "fieldId": "outputCDM”, "dataSourceId": "PredixTimeSeries” } ] } ] } Time Series Data Source & Sink
  23. 23. 23PREDIX TRANSFORM Orchestration to Asset Mapping (GEL) /turbines?filter=manufacturer=GE Energy:model=2.3-116 /turbines?filter=(groupName=NWGroup)<parent /turbines?filter=(siteName=MyWindFarm)<parent[t10] Asset grouping Search by properties Asset hierarchy
  24. 24. 24PREDIX TRANSFORM Define Asset Model Link Asset to Time Series Prepare Analytic for Upload Define I/O Mappings Define Orchestration Link Orchestration to Assets Recap
  25. 25. 25PREDIX TRANSFORM What’s Next E2: Data Services in Predix E3: Edge and Cloud Connectivity E4: Building Your First Predix App E5: Predix Security with ACS/UAA IIA9: Digital Twin & Industrial Machine Learning
  27. 27. General Electric reserves the right to make changes in specifications and features, or discontinue the product or service described at any time, without notice or obligation. These materials do not constitute a representation, warranty or documentation regarding the product or service featured. Illustrations are provided for informational purposes, and your configuration may differ. This information does not constitute legal, financial, coding, or regulatory advice in connection with your use of the product or service. Please consult your professional advisors for any such advice. GE, Predix and the GE Monogram are trademarks of General Electric Company. ©2016 General Electric Company – All rights reserved.