Making the Switch: Predictive Maintenance on Railway Switches

1. Making the switch
SparkSummit 2016

2. why Act the best way at the right moment.
how Thinking radically different and innovative about generating insights.
what
We are experts in data excellence, by delivering solutions in the field of (big)
data, data science and artificial intelligence.
Data
Integration
Data
Processing
Data
Science
Artificial
Intelligence
Advice
Custom solutions
Training

3. Anchormen
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• We specialize in data excellence:
• Consumer 360
• 24/7 Business
• Search, Match & Find
• anchormen.nl/careers

4. 8-4-2015 4
About us
Jeroen Vlek
• Lead data engineer
• Struggling with Bloodborne
(PS4)
Chris Pool
• Data scientist
• Struggling with diapers

5. Dutch railways
• Most used network in Europe
• 3,3 million journeys
• 1.157.260 daily travellers
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6. What does Strukton Rail do?
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7. Predictive Maintenance @ Strukton
• Less delays and canceling of trains
• Making Strukton the leading company in the field of rail maintenance
• Cost reduction
• Better preparation for repair personnel
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8. Switch Failures
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9. Switch Failure Causus
Frequently obstructed movements due to:
• Poor adjustment of rolling construction
• Lack of grease on slide chairs
• Bent blades
• Electrical problems (worn-out brushes, motor, etc.)
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10. Goal: Predict switch failure
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Engine start
‘Flipping’
Locked
Amperage
#Measurements

11. Challenges
Labeling
Skewed
Black
box
Non-
Intrusive
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12. Problem Definition
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Learn the deviations in the data that indicate an
upcoming malfunction

13. Data
~1500 with
sensors
~21 million
flips
100- 1000
points/ flip
50 GB data
/ year
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14. Segments
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15. Derived features
• Features that represent the curve (per segment):
• Min
• Max
• Average
• Length
• Difference compared to previous flip
• Features for entire flip
• Days since last failure
• Temperature
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16. Normalization and Aggregation
• Normalize data using sliding window
• Aggregate per day
• Min
• Max
• First
• Last
• Variance
• Average
• Count
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17. Model
• Decision tree: Will it break within the next 3 weeks or not?
• Strukton: “keep it simple and explainable”
• From days until failure to classes
• 0-2 days
• 2-7 days
• 7-21 days
• 21-55 days
• >55 days
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18. Architecture (current)
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MS-SQL MS-SQL

19. Why Spark?
• Lots of data prep and feature computation
• More switches to be added in the future
• Streaming scenarios:
• Short term failures
• Optimize personnel’s routes
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20. Results
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True negative True positive class precision
Predicted negative 798 23 97.20%
Predicted positive 1 64 98.46%
class recall 99.87% 73.56%

21. Precision vs Recall
• Precision and recall are easily explained
• Sending a mechanic is cheaper than a fine
• Recall is more important
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22. Future work
• Deep learning
• Predict the number of days (regression)
• Predict type of failures
• Less voltage
• Too disorderly
• Not locking: Too frequent
• Up/down movement
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23. Next steps
• Production
• Lambda architecture
• Nation wide roll out
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24. Questions?
• info@anchormen.nl
• www.anchormen.nl
• @anchormenBDS
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