reliability analysis of switches and crossings – a case study in swedish railway

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Division ofOperation

andMaintenanceEngineering

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

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Introduction

Railway complexity:

Mix of components with different ageWorking 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

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Introduction

The key goal is to achieve availability target cost effectively.

Availability

Reliability Maintainability Supportability

To conduct reliability analysis:

Detail failure and maintenance recorded dataDetail maintenance action doneMission profile: duty cycle and environmental characteristics

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

RAMS(reliability, availability, maintainability and safety)

tetR )(

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.

timeTotal

repairofTimestimeTotalA

RAMS

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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

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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

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BODEN

ÅNGE

GÄVLE

NORRKÖPING

STOCKHOLM

MALMÖ

GÖTEBORG

HALLSBERG

Sweden railway network

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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 Data43528 failures

Raw data without unnecessary types of turnouts42221 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)- #N/A

- Unkwown (10477 failures)

29676 failures

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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

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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







9 (out of 60) focused tracks

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10 types of turnouts generating more failures

EV-SJ50-11-1:9EV-SJ50-12-1:15EV-UIC60-1200-1:18,5EV-UIC60-1200-1:18,5 BL33EV-UIC60-300-1:9EV-UIC60-760-1:14EV-UIC60-760-1:15

EV-SJ50-11EV-SJ50-12EV-UIC60-300EV-UIC60-760EV-UIC60-1200

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Dividing into 2 types of tracks

nhsp main trackahsp diverging track

Dividing into 2 seasons

COLD from November to March (5 months)HOT from April to October (7 months)

Data classification

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Subsystems affected by failures – Hot period

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Subsystems affected by failures – Cold period

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Comparison of subsystems with more failures during the two

seasons

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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).

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Intrinsic Reliability Analysis

Is Exponential best Estimator?

Kijima

Rho = 0 Rho = 10 < Rho < 1

Work onABAO

Work onFirst Failure

Work onAGAN


YesNo


Maintenance effect analysis

POSSIBLE

Maintenance effect analysis

NOT POSSIBLE

RDAT software methodology

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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

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Instantaneous failure rate

λ failure rate

β shape parameter

Instantaneous Mean Time Between Failures

T

n

n

iiTTn

n

1

lnln

Data analysis – RDAT software output

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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

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β > 1 → MTBF ↘

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


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

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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?


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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

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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

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

β ≤1≈1 ≥1β β

RDAT results

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RDAT results

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RDAT results

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Turnouts are in serie in a track

Availability

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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

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reliability analysis of switches and crossings – a case study in swedish railway

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