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Predix Data Fabric & Digital Twin Framework- Platform for Continuous Learning (Minds & Machines 2017)
The document discusses Predix Data Fabric, which is the foundation for GE Aviation's continuous learning platform. It connects silos of airline systems data and enables ingestion from various sources in real-time, batch, and micro-batch formats. The continuous learning platform uses a digital twin framework and analytics catalog to perform functions like discovery, prediction, forecasting, and anomaly detection using techniques such as machine learning and deep learning. Two use cases are described: aircraft delay prediction to help airline disruption management, and aircraft corrosion prediction to enable predictive maintenance. The goal is to generate business value through efficiency and effectiveness improvements across airline operations.
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Predix Data Fabric & Digital Twin Framework- Platform for Continuous Learning (Minds & Machines 2017)
- 2. THOMAS BENJAMIN Chief TechnologyOfficer, GE Aviation Predix Data Fabric – Foundation for Continuous Learning DINAKAR DESHMUKH VP Data & Analytics , GE Aviation
- 3. Content Slide • Analytics& Continuous Learning Reference Architecture • Under the hood – Predix Data Fabric • Airline Disruption Management Use case • Time Series Analysis for deeper insights • Aircraft Predictive Maintenance Use case
- 4. Raw Data Modeled Data ConnectingSilo’s of Airline Systems Continuous Learning Platform - PREDIX Ingestion Batch Micro Batch Real-time Airline Operations Crew Operations Maintenance Systems Flight Operations Passenger Reservations Revenue Management Airline Integrated Operating System Digital Twin Framework Run-time Engine Orchestration Engine Analytics Catalog Learning Engine – Building Blocks Discover Operational Blind Spots Private Cloud Security Master Data Mgmt. Continuous Learning
- 5. October 30, 5 Private Cloud Ingestion Batch Micro-Batch Real-time EventHub Predix Studio Trueconnect Raw Data Area Cleansed & Transformed Modeled Area Subject Area Mart Data Science Sandbox Under the hood – Continuous Learning Platform Digital Twin Framework Analytics Catalog Workbench Orchestration Engine Discovering Structure K-Means Cluster Hierarchical Cluster Spectral Cluster Similarity Metrics Analytics CI/CD Predicting & Forecasting Linear, Stepwise Regr. Random Forest Support Vector Regr. Neural Network, DL Survival Analysis/CDM Binary Logistic Regr. Support Vector Machine Neural Net Decision Trees/Randmon forest Anomaly Detection & Diagnostics Manifold Learning Gaussian fixture Model - EM Deep Learning models Baysian Belief Networks Security Continuous Learning Master Data Management
- 6. Airline Use Case’sfor Continuous Learning
- 7. Aircraft delay prediction Airlinedisruption management – Use Case • External weather factors influence the aircraft flow • How to quantify ? • Can we use historical information and fuse multiple data sources ?
- 8. “Google” like searchfor time series Time series analysis for deeper insights • Search engine for industrial “time series” • Multi-variate time series search…scalable • Key enabler for risk assessment/root cause investigation
- 9. Aircraft corrosion prediction Aircraftpredictive maintenance – Use case Network Routing Global Environmental Data (atmospheric sea salt concentration, for example)
- 10. Business Value -Efficiency & Effectiveness Improvement Airline Operations Crew Operations Maintenance Systems Flight Operations Passenger Reservations Revenue Management Airline Integrated Operating System Continuous Learning Platform
- 11.