1. A Data Driven Approach to Railway Intervention Planning Derek Bartram Supervisors Dr. M. Burrow Prof. X. Yao

2. Contents  Current Technologies –Structure –Problems / Issues  Project –Aims –Comparison To Current Technologies –System Design  Progress To Date  Progress Problems

3. Typical Track Deterioration Angle 1 < Angle 2 < Angle 3

4. Geometry Measurements

5.  Top height  Web height  Web thickness  Ballast thickness  Ballast SD size  Corrugation wavelength  Gauge  Twist  Cant

6. Existing Technologies : Decision Support Systems

7. Expert System : Inference Engine  If (ballast_type == granite) then minimum_thickness = 50mm  If (ballast_type == sandstone) then minimum_thickness = 200mm  If (ballast_thickness < minimum_thickness) then replace_ballast

8. Expert System : Inference Engine  If (ballast_thickness < 50mm && ballast_type == granite) then replace_ballast  If (ballast_thickness < 200mm && ballast_type == sandstone) then replace_ballast

9. Decision Support Systems : Problems / Issues  Expert system only as good as the rule base  Simplified models  Possible rule / intervention flaws  Large track segments

10. Aims  Improved deterioration modelling  Improved intervention planning  Improved localised fault detection  Improved total life-cycle costing

11. Static Vs Dynamic Solutions Static solution  Guaranteed good behaviour initially  Never improves Dynamic solution  Initial behaviour potentially bad  Requires high quality existing dataset  Improves with time

12. My Project : Assumptions (1) The various possible faults for track are identifiable by unique combinations of track component deterioration

13. My Project : Assumptions (2) For each type of failure, the solution to the problem is not related to other failure types

14. My Project : Assumptions (3) Once a track sections starts failing with a particular failure type, it will continue to fail with the same failure type

15. My Solution : Tasks  Classify the various failure types  Provide a mechanism for classifying unclassified track sections  Produce a deterioration model for each failure type  Determine best intervention for each failure type

16. My Solution : Data Processing  Handle missing data  Segment data  Build data runs  Make absolute values relative

17. My Solution : Failure Types  Plot last data recording of each run in n-dimension space

18. My Solution : Classification  We know sets of individual data points and associated failure types  Failure type does not change until intervention  Decision trees  Evolutionary algorithms

19. My Solution : Classification  Decision trees

20. My Solution : Classification  Evolutionary Algorithm

21. My Solution : Work Determination For each run in failure type { Calculate fitness of subsequent intervention } Calculate average of fitness's for each intervention type Choose intervention with best average fitness

22. My Solution : Work Determination Fitness metric  Length of time before next intervention  Next failure type

23. My Solution : Deterioration Modelling  Simple model  Enhanced simple model  Evolutionary model building

24. Progress To Date  Classify the various failure types  Provide a mechanism for classifying unclassified track sections  Produce a deterioration model for each failure type  Determine best intervention for each failure type

25. My Solution : Problems  Large number of missing values in geometry data  Inconsistent / missing? work history data  Data anomalies

26. Conclusions Long term improvements over static solutions  Deterioration models  Intervention planning  Costing

27. Thank you for listening Questions?