Reliability analysis of switches and crossings: a case study in Swedish railway

(1)

Division of Maintenanceand

1

Reliability Analysis of Switches and Crossings – A Case Study in

Swedish Railway

Behzad Ghodrati, Alireza Ahmadi, Diego Galar

Division of Operation and Maintenance Engineering Luleå University of Technology, Sweden

(2)

2

Introduction

Railway complexity:



Mix of components with different age



Working together

Increase traffic volume

Higher utilization of capacity

Minimize maintenance

time

Minimize unplanned interruption

 Maintenance be performed near capacity limits

 Time between asset renewals be long enough

(3)

3

Introduction

The key goal is to achieve availability target cost effectively.

Availability

Reliability Maintainability Supportability

To conduct reliability analysis:



Detail failure and maintenance recorded data



Detail maintenance action done



Mission profile: duty cycle and environmental

characteristics

(4)

4

Reliability: Ability of an item to perform a required function under given conditions for a given time interval.

RAMS

(reliability, availability, maintainability and safety)

e t

t

R ( )  ^ ^

Availability: Ability of an item to be in a state to perform a required function under given conditions at a given instant of time or during a given time

interval, assuming that the required external resources are provided.

time Total

repair of

Times time

Total

A _ ^ 

RAMS

(5)

5

Switches

A railroad switch, turnout or set of points is a mechanical installation enabling railway trains to be guided from one track to another at a railway junction.

Name of switche in Swedish railway system: A-B-C-D (e.g.

EV – SJ50 – 11 – 1:9),

A: type of switch (single, double)Check rail B: type of railpanel

C: radius or length of switch blade D: type of angle

(6)

6



Ballast



Check rail



Cross over panel



Crossing



Fasteners



Heating system



Locking device



Rail



Rail joint (mostly protected rail joint)



Sleeper (bearer)



Snow protection



Switch blade



Switch blade position detector



Switch device (motor, gearbox, coupling, bars, etc.)

Switch and Crossing Elements

(7)

7

BODEN

ÅNGE

GÄVLE

NORRKÖPING STOCKHOLM

MALMÖ GÖTEBORG

HALLSBERG

Sweden railway network

(8)

8

Data collection and evolution

Number of registered failures Jan. 2005 – Dec. 2009

Age and location of turnouts

Switches with

numbers inferior to 50 was eliminated

Raw Data 43528 failures

Raw data without unnecessary types of turnouts 42221 failures

Installation date known

Installation date unknown

Turnouts known

Turnouts unkown

Turnouts known

Turnouts unkwown

- Changed between 05/09:

-in BESSY (1452 failures) -not in BESSY - Not changed -installation date ”0" (2004 failures) -the rest (25006 failures)

- In BESSY (30 failures) - Not in BESSY

- Changed between 05/09:

-in BESSY (31 failures) -not in BESSY - Not changed -installation date ”0" (176 failures) -the rest (977 failures)

- Unkwown (10477 failures)

29676 failures

(9)

9 24%

68% 8%

Turnouts unkown

Data not found in the different files

Data available for study

29676 failures

10477 failures

3375 failures

Final available data

Take into account the 10 types of turnouts

generating most failures and 60 tracks of interest

16627 failures

(10)

10

Tracks with more failures with at least 10 individuals asset names and at least 2 types of turnouts

Studied tracks and switches

Track

number Type of track

124 Freight track

410 Commuter trians and some freight 414 Mixed passenger and freight 420 Mixed passenger and freight 512 Mixed passenger and freight 611 Mixed passenger and freight 811 Mixed passenger and freight 813 Mixed passenger and freight 912 Mixed passenger and freight

9 (out of 60) focused tracks

(11)

11 641

715 867

1214 1745

2890 2997

3224 4291

5301

EV-SJ50-12-1:12 EV-SJ50-12-1:13 EV-UIC60-760-1:14

EV-UIC60-1200-…

EV-UIC60-1200-1:18,5 EV-UIC60-300-1:9

DKV-SJ50-…

EV-SJ50-12-1:15 EV-UIC60-760-1:15 EV-SJ50-11-1:9

0 2000 4000 6000

Number of failures

10 types of turnouts generating more failures

EV-SJ50-11-1:9 EV-SJ50-12-1:15

EV-UIC60-1200-1:18,5

EV-UIC60-1200-1:18,5 BL33 EV-UIC60-300-1:9

EV-UIC60-760-1:14 EV-UIC60-760-1:15 EV-SJ50-11 EV-SJ50-12 EV-UIC60-300 EV-UIC60-760 EV-UIC60-1200

(12)

12

Dividing into 2 types of tracks

nhsp

main track ahsp

diverging track Dividing into 2 seasons

COLD

from November to March (5 months)

HOT

from April to October (7 months)

55%

45%

Proportion of failures by season

Cold Hot

Data classification

(13)

13 3

7 10 13 31 47

62 79 98 109

120

710

1616

2057

2520

0 500 1000 1500 2000 2500 3000 Cross over panel

Check rail Ballast Sleeper (bearer) Rail Snow protection Locking device Crossing Fasteners Rail joint (mostly insulated rail joint) Heating system Switch blade Switch device (motor, gearbox,…

(blank) Switch blade position detector

Hot

Subsystems affected by

failures – Hot period

(14)

14 4

7 8 10

36 57 66 79 80

105

624

1194 1474

2521 2765

0 500 1000 1500 2000 2500 3000 Cross over panel

Sleeper (bearer) Ballast Check rail Rail Locking device Fasteners Crossing Rail joint (mostly insulated rail joint) Snow protection Switch blade Heating system Switch device (motor, gearbox,…

Switch blade position detector (blank)

Cold

Subsystems affected by

failures – Cold period

(15)

15

1194 624

1474

2765 2521

120

710

1616

2057

2520

0 1000 2000 3000

Heating system Switch blade Switch device (motor, gearbox, coupling, bars, ...)

(blank) Switch blade position

detector

HOT COLD

Comparison of subsystems with more failures during the two

seasons

(16)

16

RDAT (Reliability Data Analysis Tool) software was developed by Alstom and the University of Bordeaux (France), and deal with highly censored field data which wasn’t taken into account properly with the already existing programs.

Data analysis tool

RDAT was used to estimate the reliability functions and failure rates from field data

Four failure models have been implemented in RDAT:

exponential, Weibull, normal, and lognormal distributions.

To select the best model, a goodness-of-fit test is applied.

The maintenance quality is considered by a parameter denoted Rho:

 ρ = 1 means that the maintenance quality is AGAN (the maintenance operation is perfect).

 ρ = 0 means that the maintenance quality is ABAO (the mission can continue but leaves the item with a reliability corresponding to the age accumulated so far).

(17)

17

Intrinsic Reliability Analysis

Is Exponential best Estimator?

Kijima

Rho = 0 0 < Rho < 1 Rho = 1

Work on ABAO

Work on First Failure

Work on AGAN

Work on First Failure

Yes No

Work on First Failure Maintenance

effect analysis POSSIBLE

Maintenance effect analysis NOT POSSIBLE

RDAT software methodology

(18)

18

Data analysis – RDAT software output

Trafikverket (Swedish Railway Administration) maintenance experts consulting:

70% of cases ρ

=1

30% of cases, ρ

= 0,5-1

AGAN maintenance

ABAO maintenance

ABAO model was considered

(19)

19

 Instantaneous failure rate

λ failure rate

β shape parameter

 Instantaneous Mean Time Between Failures





T

 n









 _n

i

Ti

T n

n

1

ln ln



Data analysis – RDAT software output

(20)

20

β < 1 → MTBF ↗

 Maybe the maintenance has improved in these 5 years (Case of infant mortality: many problems at the beginning)

 The organisation learned how to deal with failures during 05/09

 Other possible explanation:

 For SJ50-11 switch point detectors taken out (less failures)

 Change of switch point detectors on the other types of turnouts (from mechanical to electrical) > reduces

number of failures in Hot and Cold

RDAT implementation and results

Growth factor Beta as a function of types of turnout and season and type of track

(21)

21

β > 1 → MTBF ↘

 ”Old equipment fails more” > Maintenance is not compensating the age of the turnout

RDAT implementation and results

Growth factor Beta as a function of types of turnout and season and type of track

(22)

22

Comparison between hot/cold

 There are much more β < 1 during COLD season, better

maintenance? More effective maintenance during winter time?

 There are much more β > 1 during HOT season, worst maintenance?

Is there any link with the number of failures avery year?

RDAT implementation and results

(23)

23

There is no relationship

between the number of failures every year and the improved or not of the maintenance for

these years.

Comparison between hot/cold

(24)

24

Values of λ and β for different types of turnouts for the 9 tracks

RDAT results

Example for tracks 124, 410 and 912 for main track and SJ50-11

(25)

25

Example for tracks 124, 410 and 912 for main track and SJ50-11

β ≈1 β

^≥1

β ≤1

RDAT results

(26)

26

0 0,00005 0,0001 0,00015 0,0002 0,00025 0,0003 0,00035 0,0004

3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60

Failure rate (λ)

Months (from January 05 to December 09)

Instantaneous failure rate (SJ50-11 and nhsp- cold)

124 410 912

RDAT results

(27)

27

0 0,00005 0,0001 0,00015 0,0002 0,00025 0,0003 0,00035 0,0004

3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60

Failure rate (λ)

Months (from January 05 to December 09)

Instantaneous failure rate (SJ50-11 and nhsp-hot)

124 410 912

RDAT results

(28)

28

Turnout 1

Turnout 2

Turnout 3

Turnout 4

Turnouts are in serie in a track

Availability

(29)

29

Conclusion

 The RAMS analysis confirms the more failure in Cold season than in Hot season

 For tracks 124, 410 and 912

 Failure rate decreasing during Cold season

 Failure rate almost constant during Hot season

 Track 512, which has the lowest availability, needs to be focused for improvement

 The RDAT software is not taking into account this parameter. However, it is possible to do a covariate analysis including this factor.

 On the most important failure contributors, which are the switch blade position detectors, switch devices, heating system in the cold season, and switch blades

(30)

30

Reliability analysis of switches and crossings: a case study in Swedish railway