dc91439: gpu accelerated iiot data science and … · bryan massie and tony demarco 5 –november...

Bryan Massie and Tony DeMarco

5 – November – 2019

DC91439: GPU ACCELERATED IIOT DATA SCIENCE AND MACHINE LEARNING IN AN ENTERPRISE DATA LAKE

2© 2019 Lockheed Martin Corporation. All rights reserved.

SPEAKERS

Bryan MassieLM Fellow Enterprise IT

Tony DeMarcoPrincipal Data Scientist AERO Enterprise Integration


OBJECTIVES

• Illustrate IIoT function at Lockheed Martin Aeronautics

• Demo analytics model delivering business value

AGENDA• Operational Reference Architecture

• Data Pipeline

• Analytics Approach

• Anomaly Detection Use Case

© 2019 Lockheed Martin Corporation. All rights reserved. 4

IIOT IS A KEY ENABLER FOR THE FACTORY OF THE FUTURE


Operational Reference ArchitectureWing Drills


Operational Reference ArchitectureWing Drills Autoclaves



Paint Robots

Autoclaves



Paint Robots

Autoclaves

Adapter

Allen BradleyFanuc

Siemens 840D

Asset Controllers



Paint Robots

Autoclaves

Adapter

Allen BradleyFanuc

Siemens 840D

Asset Controllers

Cisco Industrial Ethernet 4000 Series

Mazak Smart Box


Data Pipeline

Secure, Low-latency IIoT Data from Shop Floor to Top Floor


IIOT ANALYTICS APPROACH

Initial approach pursued during 2018

Drawbacks• Modeling requires significant

work by a knowledgeable human – knowledge of both the machine and the modeling approach

• A model for one machine class is not transferable to another i.e., not scalable)

Physics Based Modeling Machine Learning Physics Based Model Enhancement

Long Short-Term Memory Recurrent Neural Network

Upside✓Machine class agnostic

✓ Faster to produce meaningful results and identify anomalies / predict failure

✓ Scalable across machine classes

Augment Machine Learning approach with traditional physics-

based modeling

Pre-Estimate Fusion• Feature engineering• Data Selection

Post-Estimate Fusion• Ensembling of models

Traditional Approach Cognitive Approach


REFERENCE

• P. Malhotra et al. Long Short Term Memory Networks for Anomaly Detection in Time Series. Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

• https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2015-56.pdf

https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2015-56.pdf


LSTM RECURRENT NEURAL NETWORK

• RNNs “remember” past observations; allows for using a sequence as in time) as input

• LSTM is an improvement over “vanilla” RNN

• Overcomes vanishing/exploding gradient problem






ℎ𝑡−1

𝑥𝑡

𝜎ℎ𝑡






Cell state “memory”)






forget gate





• Overcomes vanishing/exploding gradient probleminput gate





• Overcomes vanishing/exploding gradient problemoutput gate


MACHINE LEARNING FOR ANOMALY DETECTION

• A common approach to anomaly detection is to compare a predicted state to a measured state• If the measured state differs from the predicted state, something may be abnormal• The predicted state comes from some physics-based or statistical model• Here, the network is learning the operation of the production machine from historical data


ANOMALY QUANTIFICATION USING ERROR DISTRIBUTION

• One variable case is simple, we just look at the magnitude of difference between predicted and observed the error)• For more than one variable, it is far more informative to look at the errors adjusted for the covariance between them

𝒆𝑛𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑 = 𝒆 − ത𝒆 𝐕𝚲−𝟏𝟐𝑽−𝟏

original

centered

align axes w/eigenvectors

scale by eigenvaluesrestore original orientation


IMPLEMENTATIONDomino Data LabR StudioNVIDIAKeraTensorFlow


EXAMPLE RESULTS


Takeaways

• IIoT is a key component/driver of the Future Factory

• IIoT data pipeline enables secure, scalable, rapid value delivery

• Machine Learning models add value by detecting anomalies; helping to avoid costly unplanned maintenance


Contact Us!

• Massie, Bryan S (US) [email protected]

• DeMarco, Antonio (US) [email protected]

mailto:[email protected]

mailto:[email protected]

dc91439: gpu accelerated iiot data science and … · bryan massie and tony demarco 5 –november...

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