http://predixtransform.com See how Machine Learning algorithms and video analytics, powered by Predix, has been used to detect defects in railways tracks. View demos build using Python and OpenCV, and an actual field video showing different cases of anomaly detection.

1. PAM3: Machine Learning in the
Railway Industry
Dattaraj J Rao – Senior Architect – GE Transportation
(@DattarajR)
Scott Nelson – Product Manager – GE Transportation

2. 2PREDIX TRANSFORM
Agenda
LocoVISION – Eyes for the Train1
Business problem and Value story
Computer Vision models
The PREDIX Advantage
DEMO
2
3
4
5

3. 3PREDIX TRANSFORM
LocoVISION – Eyes for the Train
• High-definition cameras on Locomotive
• Monitor Track geometry, Assets
• Computer Vision

4. 4PREDIX TRANSFORM
Business problem and Value story
• Track Inspection: Manual, Time consuming
• Too late to find defects - Coverage
• In-accurate PTC Asset database
Image Sources: ntsb.gov, Wikipedia.org
PTC = Positive Train Control

5. 5PREDIX TRANSFORM
Picture
Here
Geometry-based approach
• Extract geometry from images
• Isolate features of interest
• Define “rules” for anomalies
Computer Vision models
Machine Learning method
• Image as pixel data array
• Positive and Negative images
• Self-learning from data

6. 6PREDIX TRANSFORM
The PREDIX Advantage
• Common Platform – Data Acquisition to Action
• BLOB store for Video + metadata (GPS)
• On-demand + Batch Analytics in Cloud
DATA ACQUISITION PREDIX 2.0 PRESENTATION
§ COMPUTER VISION MODELS
§ MACHINE LEARNING MODELS
BLOB STORE
ACTION
PREDIX MACHINE

7. 7PREDIX TRANSFORM
DEMO
https://lviewer-dattaraj.run.aws-usw02-pr.ice.predix.io/

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