AWS IoT services help you easily monitor equipment across your industrial facilities to identify waste, such as breakdown of equipment and processes, production inefficiencies, and defects in products; identify complex events, such as equipment malfunctions; and audit security policies and take action if security threats are detected. This chalk talk walks through how to unlock OT data; compute common industrial performance metrics; build applications to analyze industrial equipment data and detect complex events; and utilize built-in device authentication and authorization, data encryption, and device security anomaly detection.

1. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Driving performance & security across your
industrial facility with AWS
Ryan Dsouza
Sr. AWS IoT Solution Architect
Amazon Web Services
S V C 2 0 9

2. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Agenda
• Lean manufacturing and OEE
• Introduction to AWS IoT SiteWise
• Calculating OEE in the cloud for all of your sites
• Reference architectures
• Industrial security
• Q&A

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Countering waste is core goal of lean manufacturing
Wastes
Defects
Over-
production
Waiting
Non-utilized
talent
Extra
processing
Transpor-
tation
Inventory
Motion

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Equipment metrics can help track waste
Overall Equipment Effectiveness (OEE)
Availability
Performance
Quality

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Prerequisites
Data collection from
facilities
Data accessibilityData labeling and
organization

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Collect,structure,andconsumedatafromindustrialsites

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• Next level of efficiencies will come from
optimizing across sites.
• Compute metrics for thousands of assets,
across hundreds of sites in near real time.
• Serve to dashboards and applications across
your organization.
• Enable end-to-end business workflows.
Drive efficiencies across sites

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OPC-UA: Liberate data from factories
Gateway hosting OPC-
UA server and AWS IoT
SiteWise software
package
PLC
Both PLC and
gateway plugged
into network
switch

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Representing your assets in AWS IoT SiteWise

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Representing your assets
Power Meter
Solar Panel
Ajax
PK78484
f(Voltage, Current)
f(Real Power)
CA/17/260-477
CA/17/260-478
Name:
Make:
Serial Number:
Real Power:
Efficiency:
Voltage:
Current:

11. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Power Meter
Solar Panel
Ajax
PK78484
f(Voltage, Current)
f(Real Power)
CA/17/260-477
CA/17/260-478
Name:
Make:
Serial Number:
Real Power:
Efficiency:
Voltage:
Current:
Asset
Representing your assets

12. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Representing your assets
Attributes
Power Meter
Solar Panel
Ajax
PK78484
f(Voltage, Current)
f(Real Power)
CA/17/260-477
CA/17/260-478
Name:
Make:
Serial Number:
Real Power:
Efficiency:
Voltage:
Current:

13. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Representing your assets
Metric
Power Meter
Solar Panel
Ajax
PK78484
f(Voltage, Current)
f(Real Power)
CA/17/260-477
CA/17/260-478
Name:
Make:
Serial Number:
Real Power:
Efficiency:
Voltage:
Current:

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Measurements
Representing your assets
Power Meter
Solar Panel
Ajax
PK78484
f(Voltage, Current)
f(Real Power)
CA/17/260-477
CA/17/260-478
Name:
Make:
Serial Number:
Real Power:
Efficiency:
Voltage:
Current:

15. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Representing your assets
Power Meter
CA1751-1A
320
watts
f(Power Meter)
f(Power Meter)
CA/17/260-477
Name:
Nominal Power:
Units:
Real Power:
Efficiency:
Power Meter:
Asset

16. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Representing your assets
Power Meter
CA1751-1A
320
watts
f(Power Meter)
f(Power Meter)
CA/17/260-477
Name:
Nominal Power:
Units:
Real Power:
Efficiency:
Power Meter:
Asset
Power Meter
Name:
Nominal Power:
Units:
Real Power:
Efficiency:
Power Meter:
Asset Template

17. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Representing your assets
Power Meter
CA1751-1A
320
watts
f(Power Meter)
f(Power Meter)
CA/17/260-477
Name:
Nominal Power:
Units:
Real Power:
Efficiency:
Power Meter:
Asset
Power Meter
Name:
Nominal Power:
Units:
Real Power:
Efficiency:
Power Meter:
Asset Template
Power Meter
CA1751-1C
320
watts
f(Power Meter)
f(Power Meter)
CA/17/260-479
Name:
Nominal Power:
Units:
Real Power:
Efficiency:
Power Meter:
Asset
Power Meter
CA1751-1B
320
watts
f(Power Meter)
f(Power Meter)
CA/17/260-478
Name:
Nominal Power:
Units:
Real Power:
Efficiency:
Power Meter:
Asset

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The recipe for OEE
Asset Packer
Data from Equipment Equipment_State (reported as numerical codes, e.g., 1024)
Good_Count
Bad_Count
Equipment State
Derivations
(Transforms)
IDLE = EQ(Equipment_State, 1024)
FAULT = EQ(Equipment_State, 1020)
STOP = EQ(Equipment_State, 1000)
RUNNING = EQ(Equipment_State, 1111)
Metrics (Aggregates)
Interval Options: 5 min, 10
min, 15 min, 30 min, 1 hour,
1 day, 1 week
IDLE_TIME = STATETIME(IDLE)
FAULT_TIME = STATETIME(FAULT)
STOP_TIME = STATETIME(STOP)
RUN_TIME = STATETIME(RUNNING)
DOWN_TIME = STOP_TIME + FAULT_TIME + IDLE_TIME
AVAILABILITY = RUN_TIME / (RUN_TIME + DOWN_TIME)
QUALITY = Last(Good_Count) / Last(Good_Count) + Last(Bad_Count)
PERFORMANCE = Last(Good_Count) / RUN_TIME * 0.2
OEE = AVAILABILITY x QUALITY x PERFORMANCE

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20. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
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)
▪ Scale 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-premises and AWS Cloud
▪ Data security & confidentiality, who has access, & how will the data be used

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AWS IoT for industrial applications

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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 IoT SiteWise and/or
AWS IoT Greengrass on
industrial gateway
AWS
MQTT
Telemetry channel
(MQTT)
OPC UA channel
(HTTPS)
HTTPS

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AWS industrial IoT reference architecture
AWS IoT GreengrassMES/SCADA
Protocol
conversion
Industrial Equipment
Industrial Equipment
Protocol
conversion
AWS IoT
Greengrass
ML Inference
AWS IoT Core
Amazon QuickSight
Amazon
Athena
IoTRule(alerts)
IoT Rule (all data)
Amazon
Kinesis Data
Analytics
Amazon Kinesis
Data Streams
Amazon
Kinesis Data
Firehose
IoT Anomaly
Data Repository
Amazon S3
Data Lake
Amazon S3
Glacier
Athena
Kinesis Data
Firehose
RealTimeand
HistoricalVisualization
Amazon
CloudWatch
Amazon Cognito
AWS CloudTrail
AWS Config
AWS IoT certificate
AWS Identity
and Access
Management (IAM)
Amazon SageMakerML Models
AWS IoT Analytics
Amazon SNS
Email
SMS
AWS IoT SiteWise

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Device connectivity optionsAWS Cloud
AWS Greengrass
SiteWise Gateway
Lambda Function
OPC UA Client
IoT Thing
PLC
IoT Thing
PLC
IoT ThingIoT ThingIoT Thing
PLC
SCADAIoT ThingIoT ThingIoT Thing IoT Thing
Lambda Function
Custom Driver
IoT Thing
PLC
IoT Thing
AWS SiteWise

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Audit Alerts MitigateDetect
Validate IoT configuration
is secure
Detect anomalies in
device behavior
Know when & what to
investigate
Remediate potential
issues
AWS IoT Device Defender

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Audit your IoT resources against a set of
built-in IoT security best practices
A set of standard audit checks act on
different IoT resources:
Certificates
Policies
Connection settings
Account settings
Schedule audits (daily, weekly) or run ad
hoc audits during vulnerable periods such
as device deployments
Scheduled
Ad hoc
Audit
Validate IoT configuration
is secure

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Create security profiles for all the devices in
your account or a group of devices that have
similar behavior characteristics
Define rule-based behaviors for security
metrics and data from connected devices
and AWS IoT Core in the security profile
Detect anomalies
Identifyanomaliesin
devicebehavior

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Alerts generated based on identified
anomalies and audit findings
Alerts sent to AWS IoT Console, CloudWatch,
and Amazon SNS
Review historical and contextual information
at individual device or resource level for audit
non-compliance or device behavior
anomaly detection
View recommended actions
to minimize the impact of potential security
issues
Alerts
Knowwhenandwhat
toinvestigate

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Take actions that patch potential security
issues and make sense
for your devices and use cases
Revoke permissions
Reboot a device
Reset factory defaults
Push security fixes
Can automate actions based on Amazon
SNS
Mitigate security issues
Remediatepotentialissues

30. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
AWS Manufacturing Reference Architecture
Greengrass
Edge/GW
S3
Data Lake
Kinesis
MES
Factory Machines
ML
Inference
IoT Core
Sage Maker
ML
QuickSight
Business
Intelligence
Athena
Historian
Storage Gateway
EMR
EBS EC2 Batch AppStreamEBS EC2
E&D Workloads
(PLM/HPC/CAE)
Enterprise Workloads
(SAP ERP/CRM)DMS RDS
Local Servers
RedShift
Data Warehouse
DataIngestion
API
SiteWise
Snowball Edge
Smart Products
DynamoDB Lambda
IoT Core
Amazon Forecast
Plant Maint. Planning
Business Functions
Greengrass
Connectors
IoT Analytics
Timestream
Outpost
IoT Events
EC2
Lambda
Business Logic

31. Thank you!
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Ryan Dsouza
Sr. AWS IoT Solution Architect