E1: Building the Digital Twin (Predix Transform 2016)

1. E1: Building the Digital Twin on Predix Kevin Yang Principal Software Architect @kevinayang Yan Or Director of Engineering yan.or@ge.com

2. 2PREDIX TRANSFORM Agenda Digital Twin on Predix Predix Asset & Analytics A Twin in Action Code Walk- through Q&A

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. 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. 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. 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. 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. 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

9. 9PREDIX TRANSFORM PREDIX ASSET & ANALYTICS

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. 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. 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. 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

14. 14PREDIX TRANSFORM A TWIN IN ACTION

15. 15PREDIX TRANSFORM

16. 16PREDIX TRANSFORM CODE WALKTHROUGH

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. 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. 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. 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. 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="http://activiti.org/bpmn"> <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. 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. 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. 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. 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 http://www.predix.io/resources

26. 26PREDIX TRANSFORM Q&A

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