http://predixtransform.com 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.

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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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.