Whereas maintenance has traditionally been a reactive, disruptive, and costly necessity, the availability of data enables asset managers to move from a reactive to a proactive model using predictive maintenance, condition based monitoring, and sensors across the network.

1. The potential of
predictive maintenance
for increasing life-cycles
and reducing costs
Markus Ossberger, WIENER LINIEN

2. KISS: More annual passes than cars!

3. New hat : Digitalisation
1. Traditional Transport Infrastructure becomes m&m
obsolete
2. Precise localisation
3. Digital maps, models
4. Real Time Information
„Car = Communication Tool“.
(Patrik Ester, VDE; in Turn!)

4. Consequences: Who is integrating Who?

5. Old hats first, Do right things right!

6. Rail Measurement Car – Source of Big Data
6
- High scan rate in 6mm intervals
- One Profile every 6mm -> Aggregation to one meter
- Larger camera visual field
-> Assessment of the Track surroundings

7. Efficient – Effective - Disruptive
Ultra Sonic Sound Ultra Sonic Sound Switches Rail Measurement Track Oiling System
Rail Grinding Rail Breakages Vehicle Load Speed Restrictions Track Geometry
Age Department Areas Broken Rail Superstructure Vehicle Speed

8. Cross effects: Driver Assistance Solution

9. What predictive needs
0 < 2 2 < 4 4 < 6 6 < 8 8 <= 10
G 45 40 36 30 28
>= 200 33 26 23 21 19
100 < 200 28 25 22 19 16
50 < 100 27 22 15 12 11
30 < 50 20 18 14 12 11
20 < 30 18 15 12 10 8
0 < 20 16 13 8
Gleisgeometrie
(Radius)
Befahrung in MGT/Jahr
• Tram: Track geometry
(left) vs. Mega Gross
Tonnes per year (above)

10. How its implemented (GIS/DB)
Year of
Construction
Vehicle Load Geometry of
Track

11. Results
Result: Increase of renwal rate to 2.76% on average to hold backlog

12. Time to decide! ROI or RAMS?

13. Alternatives (relax
budget):
Small changes, big effect
1 +10% annually
2 +20% annually
3 +30% annually

14. Next Step - Introducing BIM - Building Information Modelling
Holistic planning and evaluation
Thanks for listening!
Markus.ossberger@wienerlinien.at