Bryan Massie and Tony DeMarco
5 – November – 2019
DC91439: GPU ACCELERATED IIOT DATA SCIENCE AND MACHINE LEARNING IN AN ENTERPRISE DATA LAKE
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SPEAKERS
Bryan MassieLM Fellow Enterprise IT
Tony DeMarcoPrincipal Data Scientist AERO Enterprise Integration
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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
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IIOT IS A KEY ENABLER FOR THE FACTORY OF THE FUTURE
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Operational Reference ArchitectureWing Drills
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Operational Reference ArchitectureWing Drills Autoclaves
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Operational Reference ArchitectureWing Drills
Paint Robots
Autoclaves
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Operational Reference ArchitectureWing Drills
Paint Robots
Autoclaves
Adapter
Allen BradleyFanuc
Siemens 840D
Asset Controllers
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Operational Reference ArchitectureWing Drills
Paint Robots
Autoclaves
Adapter
Allen BradleyFanuc
Siemens 840D
Asset Controllers
Cisco Industrial Ethernet 4000 Series
Mazak Smart Box
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Data Pipeline
Secure, Low-latency IIoT Data from Shop Floor to Top Floor
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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
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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
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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
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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
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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
Cell state “memory”)
16© 2019 Lockheed Martin Corporation. All rights reserved.
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
forget gate
17© 2019 Lockheed Martin Corporation. All rights reserved.
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 probleminput gate
18© 2019 Lockheed Martin Corporation. All rights reserved.
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 problemoutput gate
19© 2019 Lockheed Martin Corporation. All rights reserved.
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
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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
21© 2019 Lockheed Martin Corporation. All rights reserved.
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
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IMPLEMENTATIONDomino Data LabR StudioNVIDIAKeraTensorFlow
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EXAMPLE RESULTS
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EXAMPLE RESULTS
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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
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Contact Us!
• Massie, Bryan S (US) [email protected]
• DeMarco, Antonio (US) [email protected]
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