Real-time machine monitoring

© 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


Lockheed Martin

Source: (“Who We Are · Lockheed Martin,” 2019)


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


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


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


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


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



.

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




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



.

On-premises

Internet

CNC machine

3D printers

lathes

Kepware

Server EX

IoT Device *

MT-Connect

Industrial

protocols

MQTT

443



MQTT

443

AWS GovCloud (US)

1

1

2 5 6 7

IoT

ru

le




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



.

On-premises

Internet

CNC machine

3D printers

lathes

Kepware

Server EX

IoT Device *

MT-Connect

Industrial

protocols

MQTT

443



MQTT

443

AWS GovCloud (US)

1

1

2 5 6 7

OT user

HTML

443

IoT

ru

le

13




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



.

On-premises

Internet

CNC machine

3D printers

lathes

Kepware

Server EX

IoT Device *

MT-Connect

Industrial

protocols

MQTT

443



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




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


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’


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


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


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

http://www.lockheedmartin.com/us/news/press-releases/2010/february/SecondMissileWarningSatel.html

https://www.lockheedmartin.com/content/dam/lockheed-martin/eo/documents/2019-lockheed-martin-fact-sheet.pdf


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?


Thank you!

Joe Marino

[email protected]

Gregg Doppelheuer

[email protected]

Stephan Gerali, Ph.D.

[email protected]

mailto:[email protected]



Real-time machine monitoring

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