real-time machine monitoring
TRANSCRIPT
© 2019 Lockheed Martin Corporation. All Rights Reserved.
Real-time machine monitoring using AWS IoT services
M F G 3 0 1
Joe Marino
Head of WW Business Development, Aerospace
Amazon Web Services
Gregg Doppelheuer
Principal Systems Architect
Lockheed Martin
Stephan Gerali, Ph.D.
Fellow – Chief Architect
Lockheed Martin
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MFG301 Real-time monitoring using AWS IoT servicesAWS re:Invent 2019 4
Agenda
❑ Overview
❑ Lockheed Martin
❑ Our journey into industrial internet of things
❑ Our IoT vision with AWS IoT services
❑ Our future enterprise timeline
❑ Deep dive
❑ Lockheed Martin Space
❑ Analyze business case for real-time monitoring of industrial devices
❑ Learn how to deploy real-time monitoring of industrial machines using AWS
❑ Describe the business value from the implemented solution
❑ Question & answer
MFG301 Real-time monitoring using AWS IoT servicesAWS re:Invent 2019 5
Lockheed Martin
Source: (“Who We Are · Lockheed Martin,” 2019)
MFG301 Real-time monitoring using AWS IoT servicesAWS re:Invent 2019 6
How did we get here …
2017-
2018
Nov
2018
Jan
2019
Feb
2019 –
July
2019
July
2019-Oct
2019
Dec 2019
&
Beyond
Establish a team
• Cross BA team
• Develop requirements
• Publish RFI
• Demos
• Refine requirements
• Develop architectural approach
(intelligent factory framework)
• Defined standards
Prove it …
• JDI event 1
• Connected 5
machines
• AWS IoT Greengrass,
Kepware, AWS IoT
Core, traverse the
Pub Cloud to AWS
Gov Cloud (US)
boundary
• 5x5 goal
Success is contagious
• Increase IFF
functionality
• Data scientist – Hack-A-
Thon
• 5x5 realized
re:Invent 2018
• Attended AWS IoT
Core session
• Talked to AWS
• Discussed our
problem solution set
• AWS conveyor, sell
IoT vision
JDI ‘R’ Us
• 4 more JDI events
• Add/refine
architecture and
functionality
• First AWS IoT Core,
AWS GovCloud (US)
deployment of AWS
IoT Greengrass
• Just secure IT
Grow it ...
• Retrofit all JDI’s
• Beta release of the
IFF
• Continue the
momentum
MFG301 Real-time monitoring using AWS IoT servicesAWS re:Invent 2019 8
Enterprise timeline
4th Qtr
2019
1st Qtr 2020
2nd Qtr
2020
3rd Qtr
2020
4th Qtr
2020
2021
2022
2023
2024
2025
✓ Implement the beta
version of the IFF at
5 sites connecting 5
machines
✓ Initial release of the IFF
✓ Accelerate connectivity
✓ Increase edge
compute/inference
✓ Each BA continues to
connect machines
✓ Derive value – longer
PM cycles, predictive
maintenance, capacity &
utilization
✓ 500 machines
connected
✓ Transition to stable
sustainment model
✓ Increase digital
twins and HMIs
✓ Integration to
core systems
✓ International
sites
connected
✓ More tool types
✓ Possible facilities
✓ Predictive analytics
✓ Additional modeling
✓ Automation (file
delivery, model
inferences)
✓ Predictive
decision-making
✓ Increase LM
competitive
advantage
MFG301 Real-time monitoring using AWS IoT servicesAWS re:Invent 2019 9
Lockheed Martin Space
Source: (“Lockheed Martin Space Overview", 2013)
Strategic & Missile Defense
Special Programs
Mission Solutions
CommercialCivil Space
Military Space
Customers
Footprint
Commercial
U.S. Navy
NASA
UK / MOD
U.S. Air Force
Classified
$9.8B2018 Sales
18,000 Employees at 193 Locations
ULAEOSAWE ZetaGEOShare
MFG301 Real-time monitoring using AWS IoT servicesAWS re:Invent 2019 10
Business case: Real-time monitoring of industrial machines
Source: (“Second Missile Warning Satellite Achieves Key Testing
Milestone At Lockheed Martin,” 2010), (“Zero Defects,” 2018)
❑ Condition-based monitoring
❑ Federated data storage
❑ Advanced data analytics
❑ Dashboard/reports
❑ Automation capabilities
MFG301 Real-time monitoring using AWS IoT servicesAWS re:Invent 2019 11
Business case: Real-time monitoring of industrial machinesOn-premises
CNC machine
3D printers
lathes
Kepware
Server EX
IoT Device *
MT-Connect
Industrial
protocols
Isolated Network
1
1
2
* Device types: Thermostats, torque wrenches, nitrogen
tanks, pick and place, automated optical inspection
machines, flying probes, thermal vacuums, ovens
MQTT
443
Transformation
(MQTT->CSV)
3Trigger
4
MQTT
443
Iso
late
d n
etw
ork
fir
ew
all
AWS IoT
Greengrass
AWS Lambda
MFG301 Real-time monitoring using AWS IoT servicesAWS re:Invent 2019 12
Business case: Real-time monitoring of industrial machines
.
On-premises
Internet
CNC machine
3D printers
lathes
Kepware
Server EX
IoT Device *
MT-Connect
Industrial
protocols
MQTT
443
Isolated Network Intranet
Off-premisesInternet
MQTT
443
AWS GovCloud (US)
1
1
2 5 6 7
* Device types: Thermostats, torque wrenches, nitrogen
tanks, pick and place, automated optical inspection
machines, flying probes, thermal vacuums, ovens
MQTT
443
Transformation
(MQTT->CSV)
3Trigger
4
MQTT
443
Iso
late
d n
etw
ork
fir
ew
all
MQTT
443
Inte
rne
t p
roxy s
erv
er
AWS IoT
Greengrass
Lambda
AWS IoT Core
MFG301 Real-time monitoring using AWS IoT servicesAWS re:Invent 2019 13
Business case: Real-time monitoring of industrial machines
.
On-premises
Internet
CNC machine
3D printers
lathes
Kepware
Server EX
IoT Device *
MT-Connect
Industrial
protocols
MQTT
443
Isolated Network Intranet
Off-premisesInternet
MQTT
443
AWS GovCloud (US)
1
1
2 5 6 7
IoT
ru
le
* Device types: Thermostats, torque wrenches, nitrogen
tanks, pick and place, automated optical inspection
machines, flying probes, thermal vacuums, ovens
MQTT
443
Transformation
(MQTT->CSV)
3Trigger
4
MQTT
443
Iso
late
d n
etw
ork
fir
ew
all
MQTT
443IoT rule
Near real-time (hot)
Data lake (cold / warm)
Trigger
IoT
ru
le
8
Streaming
SIAT
KDS
9
Inte
rne
t p
roxy s
erv
er
10
11
12
AWS IoT
Greengrass
Lambda
AWS IoT Core
Amazon Kinesis
Data Streams
Amazon Kinesis
Data Firehose
Amazon EC2
Amazon DynamoDB
Amazon S3
MFG301 Real-time monitoring using AWS IoT servicesAWS re:Invent 2019 14
Business case: Real-time monitoring of industrial machines
.
On-premises
Internet
CNC machine
3D printers
lathes
Kepware
Server EX
IoT Device *
MT-Connect
Industrial
protocols
MQTT
443
Isolated Network Intranet
Off-premisesInternet
MQTT
443
AWS GovCloud (US)
1
1
2 5 6 7
OT user
HTML
443
IoT
ru
le
13
* Device types: Thermostats, torque wrenches, nitrogen
tanks, pick and place, automated optical inspection
machines, flying probes, thermal vacuums, ovens
MQTT
443
Transformation
(MQTT->CSV)
3Trigger
4
MQTT
443
Iso
late
d n
etw
ork
fir
ew
all
MQTT
443IoT rule
Near real time (hot)
Data lake (cold / warm)
Trigger
IoT
ru
le
8
Streaming
SIAT
KDS
9
Inte
rne
t p
roxy s
erv
er
10
11
12
AWS IoT
Greengrass
Lambda
AWS IoT Core
Amazon Kinesis
Data Streams
Amazon Kinesis
Data Firehose
Amazon EC2
DynamoDB
Amazon S3
MFG301 Real-time monitoring using AWS IoT servicesAWS re:Invent 2019 15
Business case: Real-time monitoring of industrial machines
.
On-premises
Internet
CNC machine
3D printers
lathes
Kepware
Server EX
IoT Device *
MT-Connect
Industrial
protocols
MQTT
443
Isolated Network Intranet
Off-premisesInternet
MQTT
443
AWS GovCloud (US)
Tableau user
Presto (Smart Data Access)
8446
1
1
2 5 6 7
SAP HANA
SAP HANA
38615
OT user
HTML
443
IoT
ru
le
13
* Device types: Thermostats, torque wrenches, nitrogen
tanks, pick and place, automated optical inspection
machines, flying probes, thermal vacuums, ovens
MQTT
443
Transformation
(MQTT->CSV)
3Trigger
4
MQTT
443
Iso
late
d n
etw
ork
fir
ew
all
MQTT
443IoT rule
Near real time (hot)
Data lake (cold/warm)
Trigger
IoT
ru
le
8
Streaming
SIAT
KDS
9
Hive
SQL
443
Inte
rne
t p
roxy s
erv
er
10
11
1214
15
16 17
AWS IoT
Greengrass
Lambda
AWS IoT Core
Amazon Kinesis
Data Streams
Amazon Kinesis
Data Firehose
Amazon EC2
DynamoDB
Amazon S3 Amazon EMR
MFG301 Real-time monitoring using AWS IoT servicesAWS re:Invent 2019 16
Transformation: MQTT (JSON) to MTConnect (CSV)
MQTT (JSON) MTConnect (CSV)
Python Transformation Code
locations = json_object[“values”]
for location in locations:
result = result + (“Space, ,” + location[“id”].split(‘.’)[1] + “,” + location[“id”].rsplit(“.”, 1)[-1] + “, , , , , SAMPLE,” + str((datetime.utcfromtimestamp(location[“t”] / 1000).strftime(‘%Y.%m.%d %H:%M:%S’))) + “, ,” + str(location[“v”])) + ‘\n’
MFG301 Real-time monitoring using AWS IoT servicesAWS re:Invent 2019 17
Deployment: SIAT – System Invariant Analysis Technology
Source: (“LM Space CIO Discussion – SIAT, QCE, Analytics,” 2018)
❑ Customized anomaly detection
❑ Unsupervised machine learning
❑ Model normal operations
❑ Classify abnormal conditions
❑ Near real-time anomaly detection
MFG301 Real-time monitoring using AWS IoT servicesAWS re:Invent 2019 18
Business outcomes
Pay-as-you-go pricing
Scalable, responsive and reliable
Device management and data
driven insights
Built-in security for users and
devices
Deep edge, stream, and data
service processing
Seamless integration with AWS
Services
Utilization reporting for industrial
equipment with Tableau integration
Advanced anomaly detection with
NEC SIAT integration
MFG301 Real-time monitoring using AWS IoT servicesAWS re:Invent 2019 19
References
❑LM Space CIO Discussion – SIAT, QCE, Analytics. (2018, Dec 3)
❑Lockheed Martin Space Overview. (2019, Jan 1)
❑Second Missile Warning Satellite Achieves Key Testing Milestone At Lockheed Martin. (2010,
February 16). Retrieved April 8, 2018, from http://www.lockheedmartin.com/us/news/press-
releases/2010/february/SecondMissileWarningSatel.html
❑Space Systems Company Portfolio. (2013, January 1).
❑Who We Are · Lockheed Martin. (2019, January 1). Retrieved April 8, 2018, from
https://www.lockheedmartin.com/content/dam/lockheed-martin/eo/documents/2019-lockheed-
martin-fact-sheet.pdf
MFG301 Real-time monitoring using AWS IoT servicesAWS re:Invent 2019 21
Questions & FAQ’s
Have you had any insurmountable
challenges?
What are your average AWS Costs
per month?
How did you adapt your culture?
Why MT Connect?
What were your barriers to entry?
How was AWS to work with any
challenges?
What was the most difficult
connection?
How are you securing devices?
© 2019 Lockheed Martin Corporation. All Rights Reserved.
Thank you!
Joe Marino
Gregg Doppelheuer
Stephan Gerali, Ph.D.