Industrial IoT applications are rapidly emerging across industries such as oil and gas, manufacturing, and agriculture. In this chalk talk, we help you architect end-to-end solutions that will deliver value like predictive maintenance, manufacturing quality, and process monitoring. In this interactive session, we help you understand how to connect greenfield and brownfield infrastructure with AWS that leverages both AWS Greengrass (on premises) and other AWS Cloud services. Along the way, we show how the AWS Industrial IoT Reference Architecture is incorporated to build your industrial application.

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Industrial IoT Applications:
Making the Connection and Extracting Value
Ryan Dsouza
IoT Specialist SA
Amazon Web Services
I O T 3 4 2
Kevin Oleniczak
Partner SA
Amazon Web Services

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Agenda
Architecture objectives
Standards & common patterns
Common industrial use cases
Demos
Q&A

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A convergence of technology forces is changing the industrial ecosystem, triggering the digital
industrial revolution or industry 4.0
The digital industry 4.0—drivers for change
4
Profound changes in the
entire industrial
ecosystem
+
=
Connected, always-on,
intelligent, software- &
data-powered
autonomous devices
Connectivity
Cloud
Analytics and algorithms
CPU performanceChip miniaturization
Software pushInternet backbone
LTE
Technology
progress
Smart devices
Cyber physical
systems
(fourth industrial
revolution)

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Two predominant outcomes
Smart products and services are brought to market with smart
operations
Smart operations
Smart products and
services
Connectivity and internet technology
Smart sensors and actuators
Cloud processing and storage
Big data analytics and artificial intelligence

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Operations (OT)Enterprise (IT)
Manufacturing environment
IT Systems
CRM
Asset Management
ERP
Supply Chain
Finance
Maintenance
Compliance
SCADA,
DCS, etc.
Various Protocols

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L2 AB CIP/Modbus/OPC/other industrial protocols
ISA 95 & ISA 99 industrial edge architecture
L5 cloud
L4 ERP/SAP
L3 MES
L1 PLC
L0 industrial equipment
AWS Greengrass on
industrial gateway
AWS IoT
MQTT
Telemetry channel
(MQTT)
File channel
(HTTPS)

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Device connectivity options
AWS Cloud
AWS Greengrass
IoT Thing
PLC
Lambda Function
OPC UA Client
IoT Thing
PLC
IoT Thing
PLC
IoT ThingIoT ThingIoT ThingIoT Thing
PLC
IoT ThingIoT ThingIoT ThingIoT Thing IoT Thing
Lambda Function
Custom Driver

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Industrial IoT use cases and solutions
Adds real-time contextualization to the
sensor payload from external sources
Provides tools to identify correlation
factors and to predict equipment failure
Provides tools to reduce cost, improve
quality, increase yield, & optimize
industrial operations

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Industrial IoT architecture objectives?
 Achieve low latency communication between devices and hosts/gateway
 Support a mix of brownfield and greenfield connectivity (Modbus, OPC UA,
others)
 Operate without a full-time cloud dependency (pub-sub, decision-making,
ML inference, more)
 Leverage the elasticity of Amazon Web Services (AWS) (data storage,
occasional HPC, ML model training, collaboration, more)
 Scales easily as your needs grow (rollout to more machines, increased
frequency of collection, additional data captured, more)
 Secure connectivity between AWS cloud and factory or job site
 Support different alert notification solutions via on-premise and AWS cloud

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Industrial IoT reference architecture
AWS GreengrassMES/SCADA
Protocol
conversion
Industrial Equipment
Industrial Equipment
Protocol
conversion
AWS
Greengrass
ML Inference
AWS IoT/ AWS Greengrass/
AWS IoT Device Management/
AWS IoT Device Defender
Amazon QuickSight
Amazon
Athena
IoTRule(alerts)
IoT Rule (all data)
Amazon
Kinesis
Analytics
Amazon
Kinesis Streams
Amazon
Kinesis
Firehose
IoT Anomaly
Data Repository
Amazon S3
Data Lake
Amazon
Glacier
Amazon
AthenaAmazon
Kinesis Firehose
Realtimeand
HistoricalVisualization
CloudWatch
Cognito
CloudTrail
Config
IoT cert
IAM
Amazon
SageMaker
ML Models
AWS IoT Analytics
Amazon
SNS
Email
SMS

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AWS IoT Core
MQTT
server
Amazon
Kinesis Data
Firehose
ML
Inference
Machine A
AWS
Greengrass
Industrial
Equipment
Control Plane
ML Models
Amazon
SageMaker
Amazon
SNS
Email
SMS
Digital factory example—predictive maintenance
IoT
Rule
ML Models
ML
Inference
Machine B
Jupyter
Notebook
Amazon
QuickSight
AWS
IoT Analytics
Data prep
for ML
inference
Data Plane
MQTT Sparkplug B
MQTT Sparkplug B
SCADA / MES
Ignition

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AWS IoT Core
Data transport & routing
Amazon SageMaker
Process state
Product quality
Equipment health
Output forecast
Equipment
efficiency
Leak detection
Asset planning
Ops optimization
Tool productivity
AWS IoT Analytics
ML: Train and optimize in the cloud, infer at the edge, and retrain
Data aggregation, enrichment,
cleansing, time series
processing, model config
Model creation
Optimization
RetrainingData collection, model inference
AWS GreengrassAmazon
FreeRTOS

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ML framework/algorithm guidance
AWS Greengrass natively supports frameworks: MXNet, TensorFlow, and Chainer
Supervised learning:
 Linear regression
 Classification (XGBoost)
Unsupervised learning:
 Clustering (k-means)
 Anomaly detection (random cut forest)
Semi-supervised learning:
 Long-short-term memory
AWS IoT Analytics ML Model Templates:
• Detecting Contextual Anomalies
• Predictive Maintenance on Jet Engines
RCF example
Kmeans example

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OPC-UA on AWS Greengrass
Processing
AWS Lambda

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IoT Gateway – Collecting data and sending it to AWS
IoT Gateway
Out-of-Box Cloud Connectivity

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KepserverEX

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Ignition
AWS Greengrass
Lambda Function

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22
Industrial Equipment
Ignition Edge & AWS Greengrass
L0
L1
L2
L3
L4
L5
ERP/SAP (financials, etc.)
Cloud
MQTT-SP-B
AWS IoT
Telemetry channel
(MQTT)
File channel
(HTTPS)
AB CIP Protocol
Ignition Gateway
PLC
AWS Greengrass
on Raspberry Pi
AB PLC –
Micrologix 1100
Machines

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Related workshops
Tuesday, Nov 27
IOT402: Serverless Industry 4.0 & AI: Drive Business Insights from Machine Data
2:30 PM - 4:45 PM | Aria East, Plaza Level, Orovada 8
Thursday, Nov 29
IOT402: Serverless Industry 4.0 & AI: Drive Business Insights from Machine Data
2:30 PM - 4:45 PM | MGM, Level 1, Grand Ballroom 117
Thursday, Nov 29
IOT403: Building an AWS Greengrass Machine Learning Solution at the Edge
2:30 PM - 4:45 PM | MGM, Level 3, Premier Ballroom 318

25. Thank you!
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Ryan Dsouza & Kevin Oleniczak

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