Predictive maintenance holds great promise for improving industrial operations across many industries, including mining, manufacturing, oil and gas, and commercial agriculture. Industrial companies want to reap the benefits of IoT applications, but there is a lot to learn before getting started. In this session, we discuss how you can improve manufacturing plant efficiency with predictive maintenance and asset condition monitoring using AWS services, including AWS IoT Core, AWS IoT Greengrass, and Amazon SageMaker. We'll also be joined on stage with Reliance Steel & Aluminum Co., the largest metals service center operator in North America, is improving their manufacturing plant efficiency with preventative maintenance and asset management using AWS services including AWS IoT Core, AWS IoT Greengrass, and Amazon SageMaker.

1. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Atul Bargaje
Principal IoT
AWS ProServ
S V C 2 0 4
Charles Burden
Head of Business Development
TensorIoT
Long Tran
Sr. Mgr. - Head of Data Solutions
Reliance Steel & Aluminum Co.
Improve manufacturing operations
With AWS IoT and Amazon SageMaker

2. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Agenda
Overview of IoT for industrial applications
Challenges
Opportunities
Improve manufacturing operations with AWS IoT
TensorIoT/Reliance Steel predictive maintenance project

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IoT transforms traditional industrial processes
IoT brings sensors, machines, cloud computing, analytics,
and people together to improve productivity and efficiency
Manufacturing Oil & GasMining Agriculture

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IoT drives new industrial market trends
Convergence of business, process, and government
standards like Industry 4.0 and Society 5.0
Mass production
↓
Mass customization
Pay upfront
↓
Pay as you go
Buy
↓
Lease
Manual
↓
Automatic

5. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
But customers face challenges
Security
Keep devices and
data secure
Downtime
Operate at top performance even without
cloud connectivity
Legacy equipment
Onboard Greenfield and
Brownfield devices

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AWS helps you overcome challenges with software and
services for key use cases
Predictive
maintenance
Predictive
quality
Asset condition
monitoring

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Predict
failure before
business
operations
are impacted
Predictive maintenance
Reduce costs
Plan optimal maintenance work schedule
Avoid unplanned production outages
Predictive quality Asset condition monitoring

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Detect anomalies to predict
pump failure
1 Run Amazon FreeRTOS on vibration sensors to securely
collect data and connect to AWS IoT Greengrass enabled device
3 AWS IoT Analytics analyzes vibration data and adds time stamp and
device information such as serial number pulling from AWS IoT
Core. Sends updated model to the AWS IoT Greengrass enabled
device
2 The AWS IoT Greengrass enabled device runs the predictive model
locally to identify when vibrations hit dangerous levels. AWS IoT
Greengrass triggers alert to maintenance staff when anomalies are
detected. When Internet connectivity is available, the AWS IoT
Greengrass device sends data to the cloud for analytics filtering out
“normal” data
1
2
3
001
101
010
110
101
110
011
100
001
010
001
110
010
110
001010
101011
010101
Predictive maintenance Predictive quality Asset condition monitoring

9. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Oil and Gas customer explores
anomaly detection
Predictive maintenance
Problem
Inability to access IoT data, teams operated
independently using on-premises software
Solution
AWS IoT processes data from field assets, enriches it,
and stores it in a time-series optimized data store
Impact
Build and train predictive models. In the future will
deploy them on devices
Predictive quality Asset condition monitoring

10. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Quickly
spot output
quality issues
Predictive maintenance
Reduce product recalls
Increase customer satisfaction
Correct quality issues before
production impact
Predictive quality Asset condition monitoring

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Valmet lowers costs
and improves quality
Problem
Need to optimize production processes to improve quality
Solution
AWS IoT to train machine learning models for paper
quality forecasting
Impact
Combine historical models of equipment performance
with live data from current operations to produce paper with
lower costs and optimum quality
Predictive maintenancePredictive maintenance Predictive quality Asset condition monitoring

12. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Understand
asset health
Understand equipment health and status
Maximize asset utilization
Increase visibility of
machines and equipment
Predictive maintenancePredictive maintenance Predictive quality Asset condition monitoring

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Optimize truck loads3
2
1 001
101
010
110
101
001010
101011
010101
1 Sensors measure truck weight and idle time
2 Model runs on AWS IoT Greengrass enabled
device and alerts loading crews of
changes to packing mix
3 AWS IoT Analytics collects truck data and builds a
model to predict the most efficient load for
location or truck over time. Sends updates to the
model back to the AWS IoT Greengrass enabled
device
Predictive maintenancePredictive maintenance Predictive quality Asset condition monitoring

14. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Global mining
company minimizes
unexpected downtime
Problem
Needs to understand degradation equipment
Solution
Collect data from equipment, identify potholes and other problems on
mining routes that can contribute to degradation
Impact
Monitor equipment status, health, and performance to detect issues in
real-time. Detect road issues and identify equipment degradation and
minimize downtime
Predictive maintenancePredictive maintenance Predictive quality Asset condition monitoring

15. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Architecture
Amazon
Pinpoint
AWS IoT Greengrass
IoT Rule (all data)
S3 Data Lake
Kinesis Data Firehose
MES/SCADA
Protocol
conversion
Email
SMS
Factory machines
Vision
Snowball
Kinesis Data
Analytics
Protocol
conversion
ML
inference
AWS IoT Core/AWS IoT Greengrass/
AWS IoT Device Management/
AWS IoT Device Defender
Amazon SageMakerML Models
Amazon QuickSight
Kinesis Data Streams
Kinesis Data
Firehose
IoT Anomaly
Data Repository
Amazon
Athena
Amazon
Athena
IoTRule(alerts)
Realtimeand
HistoricalVisualization
CloudWatch
Amazon Cognito
CloudTrail
AWS Config
IoT Cert
IAM

16. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
What sets AWS Industrial IoT apart?
Reference architectures built for popular industrial use cases so you can quickly get started
Gather data, run sophisticated analytics, and take actions on your IoT devices from edge
to the cloud
Built-in device authentication and authorization to keep your IoT solutions secure
Reliably scale to billions of devices and trillions of messages
Sophisticated analytics and pre-built templates so you can build Machine Learning (ML) models and ML
inference at the edge
Industrial customers have achieved business outcomes such as increased revenue and
faster time to market
Rich ecosystem of technology and consulting partners

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Accelerating Outcomes
WE TAKE CARE OF CLOUD, SO YOU CAN TAKE CARE OF BUSINESS

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Introductio
n All-in on AWS Led by ex-AWS,
ex-Googlers
100% AWS Certified
AWS Advanced Consulting
Partner
Proven success in North
America, EMEA, and APAC
Our company

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Competenci
es
Edge computing
IoT
Big data
AI & ML
Conversational interfaces (Alexa & Lex)
Our focus
Containers, microservices & serverless computing

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Competenci
es
Our customers

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• Fortune 500 company (ranked 305) founded in 1939,
went public in 1994
• Largest metals service center companies in North
America
• 300+ locations across 40 states and 13 countries
• 100,000+ metal products
• Carbon steel, stainless, alloy, aluminum
• Bar, tubing, plate, sheet, coil
• Value-added metals processing services
• 125,000+ customers
▪ Manufacturing / Energy
▪ Aerospace / Transportation
▪ Construction
Reliance Steel & Aluminum overview

22. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Business use case
• Aging mission critical & highly capitalized equipment spend
• Predictive maintenance
• Workflow automation and drive OpEx
• Asset management
• Site planning and process change management
• Connected distribution centers
• Data driven decisions at each level

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Building comprehensive IoT
• Enabling AWS IoT on connected devices
• Connecting connection-capable devices
• Adding sensors to devices that are not connected
• Getting logs from devices that are not connected and leveraging them

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Achieving business goals
• Secondary sensor network for enhanced monitoring
• Integration with industrial protocols OPC UA to get machine data
• Leverage existing logs and maintenance data to gain analytic insight
• Implement an automated ticketing workflow system
• Combine data from each source above to derive predictive maintenance

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Implementation details
• Leverages managed services on AWS
• Fully serverless architecture
• Full-fledged asset management
• UI-driven, automatic provisioning of devices
• Multiple personas and customizable dashboards

26. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Outcomes
• Enable business logic
➢ Alerts
➢ Prioritized maintenance
• Use data better with ML
➢ Predictive Analysis
➢ Anomaly Detection
• Better understanding of the IoT fleet
➢ How many devices? How are they performing?
• Better security and easier updates
➢ Remote and automatic
• More and better data
➢ Real-time insights from more sources

27. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Enable the future
• Let the device tell you when it needs repair
• Automated and just-in-time part delivery
• Use Augmented Reality to see an overlay of device details and status on a factory floor
• Alexa Integration to get voice driven insights
• Improved safety and efficiency assisted by sensor data
• Leverage and deploy the latest machine learning insights to drive business value

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Thank you!
S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

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31. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Appendix
Full reference architectures

32. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Predictive maintenance architecture
Pinpoint
AWS IoT Greengrass
IoT Rule (all data)
S3 Data Lake
Amazon Kinesis Firehose
MES/SCADA
Protocol
conversion
Email
SMS
Factory Machines
Vision
Snowball
Kinesis Data
Analytics
Protocol
conversion
ML
Inference
AWS IoT Core/AWS IoT Greengrass/
AWS IoT Device Management/
AWS IoT Device Defender
Amazon SageMakerML Models
Amazon QuickSight
Amazon Kinesis Streams
Kinesis Data
Firehose
IoT Anomaly
Data Repository
Amazon
Athena
Amazon
Athena
IoTRule(alerts)
Realtimeand
HistoricalVisualization
CloudWatch
Cognito
CloudTrail
Config
IoT Cert
IAM

33. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
CloudWatch
Cognito
CloudTrail
Config
IoT Cert
IAM
Predictive Maintenance Architecture with AWS IoT Analytics
Pinpoint
AWS IoT Greengrass
IoT Rule (all data)
MES/SCADA
Email
SMS
Factory Machines
Vision
Protocol
conversion
Amazon SageMakerML Models
Amazon QuickSight
AWS IoT AnalyticsIoTRule(alerts)
Jupyter Notebook
Anomalies
Protocol
conversion
ML
Inference
AWS IoT Core/AWS IoT Greengrass/
AWS IoT Device Management/
AWS IoT Device Defender

34. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
CloudWatch
Cognito
CloudTrail
Config
IoT Cert
IAM
Predictive Quality Architecture
Pinpoint
AWS IoT Greengrass
IoT Rule (all data)
S3 Data Lake
Amazon Kinesis Firehose
MES/SCADA
Protocol
conversion
Email
SMS
Factory Machines
Vision
Snowball
Kinesis
Analytics
Protocol
conversion
Amazon SageMakerML Models
Amazon QuickSight
Amazon Kinesis Streams
Amazon
Kinesis Firehose
IoT Anomaly
Data Repository
Amazon
Athena
Amazon
Athena
IoTRule(alerts)
Realtimeand
HistoricalVisualization
S3
Analytics short term
data repository
AWS IoT Core/AWS IoT Greengrass/
AWS IoT Device Management/
AWS IoT Device Defender

35. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
CloudWatch
Cognito
CloudTrail
Config
IoT Cert
IAM
Predictive Quality Architecture with AWS IoT Analytics
Pinpoint
AWS IoT Greengrass
IoT Rule (all data)
MES/SCADA
Protocol
conversion
Email
SMS
Factory Machines
Vision
Protocol
conversion
ML
Inference
ML Models
Amazon QuickSight
AWS IoT AnalyticsIoTRule(alerts)
Jupyter Notebook
Vision system
images
Amazon SageMaker/
Jupyter Notebook
Anomalies
AWS IoT Core/AWS IoT Greengrass/
AWS IoT Device Management/
AWS IoT Device Defender

36. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
CloudWatch
Cognito
CloudTrail
config
IoT Cert
IAM
Asset Condition Monitoring Architecture
Pinpoint
AWS IoT Greengrass
IoT Rule (all data)
S3 Data Lake
Amazon Kinesis Firehose
MES/SCADA
Protocol
conversion
Email
SMS
Factory Machines
Vision
Snowball
Kinesis
Analytics
Protocol
conversion
Amazon QuickSight
Amazon Kinesis Streams
Kinesis
Firehose
IoT Anomaly
Data Repository
Amazon
Athena
Amazon
Athena
IoTRule(alerts)
Realtimeand
HistoricalVisualization
AWS IoT Core/AWS IoT Greengrass/
AWS IoT Device Management/
AWS IoT Device Defender

37. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
CloudWatch
Cognito
CloudTrail
config
IoT Cert
IAM
Asset Condition Monitoring Architecture with AWS IoT Analytics
Pinpoint
AWS IoT Greengrass
IoT Rule (all data)
MES/SCADA
Protocol
conversion
Email
SMS
Factory Machines
Vision
Protocol
conversion
Amazon QuickSight
AWS IoT AnalyticsIoTRule(alerts)
Jupyter Notebook
Anomalies
AWS IoT Core/AWS IoT Greengrass/
AWS IoT Device Management/
AWS IoT Device Defender

38. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Appendix
Customer Success Stories

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Problem
Enel was looking to manage network distribution at
the edge. They were seeking to collect sensor and
meter data over 500,000 cabins distributed across
the territory to measure energy consumption,
monitor network behavior in real time, and track
effects of seismic waves and earthquakes.
Solution
Enel implemented AWS Greengrass to collect,
convey, and process the largest amount of data to
trigger actions, govern activities, respond to
anomalies, and promote new services.
Impact
The project is the widest so far in Europe and
provides Enel an opportunity to leverage data to
design a better future.

40. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Modjoul is a data invention company
for wearable technology that is
focused on keeping employees safe.
Modjoul is using AWS IoT Analytics to
help keep industrial workers safe,
whether they’re working in or out of a
vehicle. In an eight-hour shift, they
collect data 28,800 times per day from
their connected activity tracker worn
by each of their employees that
includes 40 metrics including heart
rate and activity level. With AWS IoT
Analytics, they not only analyze all that
health data, but also enrich it with
location and environmental data, such
as outdoor temperature, to get
accurate analytics that prevent injuries
and save lives.
Problem
Healthcare poses a number of challenges when
connecting devices including patient privacy,
industry standards and protocols, and the
management of personal and population data.
Solution
The Philips HealthSuite digital platform (HSDP) is
built to face the challenges of connecting and
supporting healthcare devices. HSDP Connect
Services leverage AWS IoT services to manage,
update, & remotely monitor data from smart
devices.
Impact
HSDP Connect Services currently enable over 11
million device connectivity solutions. By using
AWS IoT services, HSDP Connect is reducing
connectivity costs by 46%.

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Problem
Pentair provides water filtration systems
equipped with sensors to fish farms and large
industrial brewing customers. Most of their
industrial customers are located in geographies
with unreliable internet connectivity. They need
to send data from sensors and devices to the
cloud while continuously maintaining
connection which is challenging in remote areas.
Solution
From Pentair’s water filtration systems, data is
sent to AWS IoT Core. When connectivity is
limited, AWS Greengrass provides Pentair with a
local connection so data is never lost.
Impact
Pentair can make decisions in near real-time
that impact the health of its devices but also the
health of the fish which in turn, results in better
yields, prevents the spread of disease and
lowers cost of operations.