sustrail - wp4 sustainable track final conference meeting · comparing the wear data of the...
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SUSTRAIL - WP4 Sustainable Track
Final Conference Meeting Brussels, Belgium – 21st May 2015
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WP4 Sustainable Track
1, Work Package 4 Objectives:
Facilitate the need for the railway infrastructure to
accommodate more traffic, whilst at the same time
reducing deterioration of track and wheels through
increasing the resistance of the track to the loads
imposed on it by vehicles.
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SUSTRAIL: The Project Structure
8 Work Packages divided into
four main phases:-
1) Benchmarking and
Requirements
2) RTD activities on vehicles
and track
3) Demonstration
4) Dissemination (WP7) and
Project Coordination (WP8)
WP4 Sustainable Track
Sleeper
Rail Track Transmitted Forces
Sub-ballast
Base Layer
Vehicle – WP3 Task 4.1
Task 4.2
Task 4.3
& 4.4
Ballast
Wayside
Station Task 4.5
Pictorial Representation of links among WP4 Tasks
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WP4 Sustainable Track
WP 4: Sustainable Track
WP4 Deliverables
The WP4 Deliverables have all been submitted
SUSTRAIL
Sustainable Track towards a “zero”
maintenance track
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Sustainable Track
Where we have to impact: Maintenance + renewal of a typical
railway track and represents 50–
60% of the total costs of over its
service life
Geometry deterioration can even
increase it
Climate effects shows how
vulnerable the track/subgrade is
Courtesy of Network Rail
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8 Sustrail Overview
18%
16%
34%
14%
18%
Operation (excluding traction power)
Maintenance
Renewals
Enhancement
Interest costs
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Performance based design principles - Why
AIM: Characterizing the variable involved in the design of the system as probabilistic variables, evolving according to a specified probability function (PDF)
Reducing the uncertainties and variability of design across the lifetime of the infrastructure (Life Cycle Approach)
Optimising track design to deliver improved track geometry retention
Making use of condition monitoring techniques to improve maintenance solutions and technologies
Estimating failure modes for each element (or component) of a system
Moving from Robustness to Resilience
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Performance based design principles 9
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Performance based design principles - How
Workflow of “performance based” activities
Performance based design principles
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Performance based design principles
Moving towards Performance Based Design Principles
Deterministic Probabilistic
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Performance based design principles 11
Content of
information Accuracy
Deterministic
Probabilistic
Δperformance
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Performance based design principles
Risk approch in performance based design methods
A risk approach can be suitable to analyze and evaluate the
failures of a system (e.g. the railway track/infrastructure), its
causes and associated effects
Risk can be calculated as
R = CF = Pf C
R :
CF :
Pf :
C :
Risk
Cost of Failure
Probability of Failure
Consequence of Failure
Can vary dramatically depending on which costs are included:
Structure costs
System & Site costs
Costs due to loss of productivity
Costs due to legislation/code change
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Performance based design principles 12
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Performance based design principles
Reliability The calculation of Pf is strongly connected to reliability
concepts and formulations.
Reliability is a mathematical formulation of Pf
Physical Quantity of Interest, x
Pro
bab
ilit
y D
ensi
ty
f L (x)f R (x)
LOADRESISTANCE
Component
System
(Series)
(Parallel)
1or 0or L
RLRLR
LR
LRs drdllfrfLRPp )()()0(
Fig. 10. Gaussian PDF distribution of a system, within a reliability approach
Resistance
Load
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Performance based design principles 13
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Performance based design principles
This imply introducing the concept of reliability, but how?
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Performance based design principles
Using monitoring to improve reliability
Monitoring can provide statistical information necessary to
employ reliability-based analysis
Monitoring provide the capability to reduce the uncertainty
associated with the initial characterization of the random
variables, to reflect changes in the random variables over
time by updating them and their distribution
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Performance based design principles
Using monitoring to improve reliability
The idea is to use monitoring data as input values to the
random, observing the evolution of the PDF curves and of
the Pfail values
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Performance based design principles 16
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Performance based design principles - Approach
But what can be done to account for the reliability of a system starting from the analysis of the potential failure modes, causes and effects?
A failure mode and effects analysis (FMEA)
The FMEA is the right method to identify potential failure modes based on:
past experience where a benchmark exists
common failure mechanism logic and expertise of the panel providing the inputs (for new application/process)
The FMEA enables to review/optimize the design process in such a way that the failures can be minimized
The success of an Failure Analysis is strongly dependent on
the quality and levels details of the inputs provided
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Performance based design principles
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Performance based design principles - Approach
The FMEA accounts for:
Severity of a failure (S)
Occurrence of a failure (O)
Detection of a failure (D)
The FMEA helps in reducing costs, which are directly/indirectly linked with the cost of a failure
Through the FMEA one can identifies and better quantifies the effects of a failure, thus recommending corrective actions to reduce the impacts or to restore normalcy
The FMEA provide an objective outcome that is the calculation of a Risk Priority Index (RPN) given as:
RPN=Severity * Occurrence * Detection = S*O*D
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Performance based design principles
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Performance based design principles - FMEA
Main steps of an FMEA (vertical approach – bottom/up)
Step1 • Subdivide the system in components/processes
Step2
• Identify for each component/process the associated failure modes, their causes and their effects
Step3
• Associate to each potential failure mode a severity, occurrence and detection in a range from 0 to 10
Step4
• Calculate a risk priority number (RPN) as Severity*Occurrence*Detection and rank the failure modes to identify which one has major impact on the system
Step5
• Provide recommended actions and target responsibilities (specifications, quality procedures, etc) to minimize the impact of the most critical failures
Performance based design principles
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Performance based design principles - FMEA
However Check & Actions are need to be performed in a
recursive and iterative way within the vertical approach
This work, through Check & Actions (made by FMEA experts and the inputs
providers) leads to the results of a FMEA
Iteration are made among steps 2 and 4
Performance based design principles
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Performance based design principles - Approach
How the FMEA is applied to the railway track Three main phases:
Hazard identification: FMEA on track components
Identification of innovations to solve previously identified failures
Selection of improvements for reducing track maintenance
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Step1
• Identifying track components, their failure modes (causes) and associated effects by means of the FMEA
Step2
• Implementing a FMEA analysis for components at critical locations (bridges abutments, crossing, etc..)
Step3
• Ranking failure risks on the basis of severity, occurrence and delectability by means of a Risk Priority Index (RPI) + cost quantifications
Step4
• Identification of innovations that solve pre-identified failures, that are economically sustainable (simplified cost analysis)
Step5 • Clustering and selecting the “kit” of improvements towards a more sustainable track
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Performance based design principles - Approach
How to make use of the FMEA results and link them with innovations
There are three main ways FMEA results and innovations can be linked.
Innovations are those that:
Minimize the severity of a failure (we accept to have a failure but we reduce its impacts)
Reduce the occurrence of a failure (we accept to have a failure but we plan for actions so that the failure can happen less frequently)
Improve the detection of the failure (e.g. by implementing condition monitoring solutions)
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Performance based design principles - Approach
Methodology
Quantify the costs associated to the highly ranked RPI (e.g. most relevant failures) – calculate a RCPI
Identify the “innovations” that can lead to a reduction of the RCPI (e.g. “object function”)
Categorise the innovations by:
Innovations that can minimize the severity (e.g. maintenance activities, re-design, optimization, etc.)
Innovations that can reduce the occurrence (e.g. optimization of the track system and geometry, maintenance, etc.)
Innovations that can improve the detection (e.g. condition monitoring solutions, etc.)
Quantify the costs associated to implementing an innovation that can reduce the RPI
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Performance based design principles - Results
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Performance based design principles
Identification of failures and their priority
Track Item Main
Function Locations Potential Failure Mode(s) Potential Cause(s) of Failure
Potential Effect(s) of
Failure
FIC
Rail carries load plain line
brittle fracture cold environment, over stressing
in CWR
vertical fracture, crack
in rail cap, derailment
R1
RCF curves, bogie stiffness, track
irregularities
high maintenance, rail
breaks
R2
Earthworks supports
railway
Embankments
embankment erosion flooding, water, weather, poor
maintenance
collapse or dip in track,
derailment
E1
shrink-swell seasonal moisture content poor ride, high
maintenance,
derailment
E2
cutting slopes embankment slip onto track
vegetation, weather, poor
construction originally, poor
maintenance
soil/rocks/tree stumps
on line, derailment,
landslide
E3
Track guides
vehicles plain line poor geometry
Component deterioration and
general geometry degradation
under traffic
premature component
failure, poor ride, high
maintenance,
derailment
T1
Structures supports
railway tunnels lining failure, rock-falls
ageing asset, erosion of
mortar/brickwork by water/ice,
geological faults
line closure, loss of
service, derailment
S1
S&C supports
railway junctions switch rail wear passing of vehicles
poor ride, flange climb
leading to derailment
SC
Joints connects rails along the line rail joint failure impact damage, fishplate breaks derailment, loss of
capacity/service
J1
Rail pads holds rail in
place along the line worn or missing rail pad
traffic and impacts/ poor
maintenance
poor ride, propagation
of rail foot failure,
derailment
RP
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Performance based design principles - Results
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Failures priorities code
Failure
Impact/Performance Failure Management
Cost
Analysis
Cost Impact
on Risk
FIC
Sev
erit
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)
Occ
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(O)
Det
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(D)
RPI Recommended Actions
Cost
(C
)
RCPI
R1 9 6 3 162
Stress free temperature management, Ultrasonic testing
5 810
R2 9 7 5 315 Visual/ultrasonic/eddy current testing 4 1260
E1 8 6 3 132
Water management, Flood defences, Protective layers
8.5 1122
E2 6 3 8 144
Vegetation management, Water management
4.5 648
E3 9 5 4 180
Vegetation management avoiding coppicing, Soil nailing,
Protective layers of geotextiles 8.5 1530
T1 5 5 3 75 Reduce speed on the track 6 450
S1 9 5 4 162
Condition monitoring, sprayed concrete linings
5.5 891
SC 7 5 2 53 Train borne condition monitoring, inspection 5 263
J1 8 4 3 79 Renewal or conversion in-situ to CWR 3.5 276
RP 6 3 6 108
Routine programme of pad replacement, use of as hard a pad as allowable
3 324
High Impact – Failures that cause serious losses (out of service) and require high costs to restore normal service
Moderate Impact – Failures that cause moderate losses and involve moderate costs to restore normal service
Medium/Low Impact – Failures that cause from low to medium losses and involve low/medium costs to restore normal service
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Performance based design principles - Results
From priorities to selection of where improvements are needed (“innovations”)
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Performance based design principles
Rail Increase rail cross section (reduce occurrence)
Rail grinding (minimize severity) through Improved predictions of RCF damage
Improved rail material (reduce occurrence) through the use of premium rail
steel for improved rail materials
Earthworks Slope stabilization (minimize severity) through multifunctional geotextiles
Resilient earthworks (minimize severity) through new designs and/or
technologies for substructure, validation of previous innovation in the domain
Track Geometry monitoring on appropriate frequency (improve detection) through
improved track geometry monitoring techniques
Geometry monitoring on appropriate frequency (improve detection) through
improved methods for geometry degradation prediction
S&C Install lubrication system (minimize the severity) through improved lubrication
regime for slide plates under switch rails
Ultrasonic testing (improve the detection)
Improved rail material (reduce occurrence) through Optimised flexibility of S&C
Joints Correct problem (reduce occurrence) and monitor (improve detection) by
changing fastening
Rail pads Improved rail pad life (reduce occurrence) by specifications/ recommendations
on geometry, materials, etc. (eventually new designs)
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Performance based design principles - Results
Where Sustrail has aimed providing its contribution
Rail:
Reduce occurrence (improved rail material) through the use of premium rail steel for improved rail materials accompanied by guidelines for novel steels and welding processes
Minimize severity (rail grinding) through improving methods for prediction of RCF damage
Earthworks
Minimize severity (slope stabilization) and Improve detection (movements sensors) through multifunctional geotextiles (reinforcing + monitoring capabilities)
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Performance based design principles - Results
Where Sustrail has aimed providing its contribution
Track
Improve detection (Geometry monitoring on appropriate frequency) through optimized maintenance scheduling using novel methods for degradation prediction
Minimize severity (maintenance fix before unacceptable levels are reached) through force information from track condition monitoring systems to affect the train operations
S&C:
Minimize the severity (installation of lubrication system) through improved lubrication regime for slide plates under switch rails
Reduce the occurrence (self fault diagnosis) through guidelines on parameters variation on wheel, track geometry, crossing shape and support conditions
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Performance based design principles - Conclusions
Concluding, when looking for performance based design method, first a Failure Mode and Effect Analysis (FMEA) need to be carried out, to:
Establish a benchmark (e.g. a baseline)
Understanding where/how improvements are needed in terms of reliability, availability, maintainability and safety
Collect precise information capturing the engineering knowledge
Identify the weak points and the potential associated failures
Minimize late changes and associated cost by identifying how/where improvements can be made to restore normalcy or to improve design and performances
This is a catalyst element for teamwork and problem solving
Performance based design principles
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Supportive Ballast and Substrate
Donato Zangani
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Supportive ballast and substrate
Summary and Aim: Identify the impact of substrate stiffness variation on track geometry deterioration and other track defects.
Focus on the role of structures (eg. bridges and embankments) on track stiffness and the ability of the railway to bear the loads to which it will be subjected.
Outputs will reduce track geometry deterioration and contribute to optimisation of LCC and include:
the production of a system for substructure classification;
guidelines for the selection of piling and geotextiles;
guidelines for the treatment of transition zones
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The approach: A numerical approach based on the use of the Finite Element Model (FEM) is adopted
The FEM of the railway allows to explicit modelling of rails, sleepers, ballast, sub-ballast and the subgrade
Main outcomes: Investigation on the substructure deformations due to:
Different trains passing over it (i.e. different axle loads and speeds)
Different types of reinforcing/retrofitting solutions to be considered
Investigation whether track damage and safety issues would become more likely when different traffic conditions and track substructure would be encountered
Capability of investigating transition zones where the stiffness of the track changes significantly over a short distance. A dynamic FEM can be used to investigate different design and maintenance implications for these transition zones
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A Numerical Approach
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Dimitrovgrad-Svilengrad cross section km 253+200
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Case Study
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Track System Geometry
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Details of Finite Element Model
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Stress Distribution analysis
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Stress Distribution analysis
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Influence of Speed/Axle load
Loose sand as ground soil
V=70 km/h
Axle load=10t
Loose sand as ground soil
V=140 km/h
Axle load=22.5t
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Influence of Speed/Axle load
Loose sand as ground soil
V=140 km/h
Axle load=22.5t
Loose sand as ground soil
V=70 km/h
Axle load=10t
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Influence of stiffness change in transition zones
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Influence of stiffness change in transition zones
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Influence of stiffness change in transition zones
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Influence of stiffness change in transition zones
A: soil
B: soil_top
C: sub_ballast
D: ballast
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Subgrade Reinforcement with Geosynthetics
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F F
e
T1
Position [m]
Str
ain
[m
e]
Reading Unit
Distributed Sensor 0m
1m 100m
1000m
20km
T1
e
T2
T2
Position [m]
Te
mp
. [°
C]
Sensing Distributed Technology
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Multifunctional Geotextiles
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Testing the selected Innovation
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Assesment of the use of multifunctional geotextiles
Test site in 2014
Sensors
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Multifunctional Geotextiles
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Multifunctional Geotextiles
The benefits of using sensor embedded geogrids within the railway substructure can encompass:
Indicate impending failure
Provide a warning
Reveal unknowns
Evaluate critical design assumptions
Assess contractor’s means and methods
Minimize damage to adjacent structures
Control construction
Provide data to help select remedial methods to fix problems
Document performance for assessing damages
Inform stakeholders
Satisfy regulators
Reduce litigation
Advance state-of-knowledge
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Thank you for your attention!
SUSTRAIL Final Dissemination Event
WP4, T4.4 – Switches and Crossings Yann Bezin, University of Huddersfield
21st May 2015,
Brussels, Royal Flemish Academy
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Facing move Trailing move
Turning
radius
Crossing angle
Point Operating
Equipment (POE)
switch heels
and heel blocks
switch rails
points
stock railsclosure rails
check rails
flangeway
crossing nose
wing rails
Through route
Switch panel Closure panel Crossing panel
S&C layout and areas of work U
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WP44 Switches and Crossings 52
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Panel Component Failures Causes
Cast manganese
Casting transverse fatigue crack (foot or nose) poor support, high dynamic forces, design flaw
Crossing nose wear, plastic deformation, shelling and spalling
high stress intensity wheel rail contact conditions, poor compliance of
wheel and rail geometry and high dynamic interaction
Wing rail wear, plastic deformation, shelling and spalling
high stress intensity wheel rail contact conditions, poor compliance of
wheel and rail geometry and high dynamic interaction
bearers fatigue cracking, voids poor support condition and maintenance, high track dynamic interaction
switch rails lipping, head checks, squats, wear
high stress intensity wheel rail contact conditions, poor compliance of
wheel and rail geometry and high dynamic interaction
points all the above + fracture by fatigue as above + poor connection to stock rail or obstruction
stock rails lipping, head checks, squats, wear, spalling
high stress intensity wheel rail contact conditions, poor compliance of
wheel and rail geometry and high dynamic interaction
slide plates poor movement (high friction) and ceisure
poor support maintenance (differential settlement and alignment), poor
lubrication, contamination
bearers fatigue cracking, voids poor support condition and maintenance, high track dynamic interaction
motor, drive & lock
mechanisms
motor/mechanism operation failure, loosening
of element and loss of accuracy…
obstruction, water ingress, poor maintenance, interaction between track
vibration and POE fixings
backdrive
mechanism loose elements and poor adjustment
obstruction,poor maintenance, interaction between track vibration and
POE fixings
stretcher bars loose, cracked or broken fixings
poor maintenance and high dynamic vibration (vehicle-track interaction
and track-component interaction)
control, electronic,
hydraulics &
detection
failed sensors/relay, loose/damaged/leaking
hydraulics
environmental damage (water/ice, wind…), high dynamic vibration, poor
installation and maintenance
Cro
ssin
gP
oin
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Eq
uip
men
tsS
witc
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S&C failure matrix and justification U
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S&C failure matrix and justification U
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WP44 Switches and Crossings 54
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Drive and Lock Mechanism: - POE SotA and failure analysis
Materials: - Premium steel in S&C components
- Slide plates lubrication
Geometrical interfaces (track-vehicle) - Understanding wheel-rail conformity and impact on
vertical damage
Support stiffness: - Using added resilience to mitigate vertical
load damage in load transfer areas
UK
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Task 4.4.1 – Point Operating Equipment
Failure analysis based on selected UK route (1yr)
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Count of Fault/Incident
BML1
DCL
EMP
LEC2
LEC3
LEC4
LEC5
Grand Total
NO CAUSE FOUND 20 27
16 2 15 4 84
NULL 24 15 7 10 2 5 3 66
BACKDRIVE MECHANISM 14 13
10 3 9 4 53
SUPPLIMENTARY DETECTION 8 6
20 2 7
43
LOCKING MECHANISM 9 5
1 2 14 3 34
CLAMPLOCK MECHANISM 6 12 1 2
4 4 29
DETECTION RODS 4 8 1 6 4 3
26
STRETCHER BAR FAILURES 10 7
2 2 4
25
RAIL POSITION SENSOR (LVDT) 2 2
17
21
DETECTION ASSEMBLY 2 6 2 4
1
15
SWITCH RAIL 1 11
1 13
SIGNALLING RELAY 4 7
2
13
DRIVE ROD 4 3 2 1 1
1 12
POINT MOTOR 6 2
3
1
12
ACTUATOR / HOSES 3 8
1 12
POWER SUPPLY 2 6
2
1
11
STAFF ERROR 3 1 2 3 1
10
DETECTION/DRIVE CONTACTS/CAMS 2 1
2 3
8
DRIVE MECHANISM 4
1
3
8
BASEPLATES / CHAIRS 1 5
1 1
8
POWER PACK 1 5
1
7
ELECTRONIC CONTROL UNIT 3 2
2
7
INTERNAL LOCATION WIRING
2
2
2 6
SIGNALLING TAIL CABLES 3 1
1
1 6
CIRCUIT CONTROLLER / WIRING 2 2 1
1
6
RODDING RUN
5 1
6
BALLAST 1 1 1 1
1 5
BASEPLATE / CHAIRS
3
1
4
BLOCK/PINS/BOLTS/STUDS 3
1 4
SNUBBING MECHANISM 3 1
4
DETECTION UNITS 1 2
1
4
DRIVE SHAFT
1
2
3
HANDCRANK MECHANISM 1 2
3
CLUTCH 1 1
2
POINT MACHINE CASE
1
1
2
BRAKE ASSEMBLY 1 1
2
SO HYDRIVE BACKDRIVE ACTUATOR
2
2
HYDRAULIC ACCUMULATOR UNIT
2
2
POINT DETECTOR CASE 1
1
DISCONNECTION BOX
1
1
SWITCH RAIL DRIVE BRACKET
1
1
TIE BAR
1
1
GEARING
1
1
13% of all faults with
no identifiable cause,
still cause disruption
10% are null (error in
data entering,
intermittent, not
serious enough…)
Targeted future improvement in POE CM to increase reliability need to improve:
• diagnosis of faults and reducing the occurrences of false failure
indications, backed up by a robust fault and failure reporting system.
• reliability of, or eliminating, the types of mechanical linkages/connections
which are associated with the majority of the actual mechanism failures
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Task 4.4.2 – Materials: Premium Steel G
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(a)
(b)
Microstructure of
(a) R260 grade steel and (b) HP335 grade steel
(a) R260 switch blade (b) Bainitic B320 switch blade
Photograph from a test site with 120kph, 20t axle load after a
similar level of traffic.
• Evidence of wear
resistance of harden rail
steel in switch blades
• Also some evidence of
better behaviour for RCF
BENEFITS
• weld repair require further
testing
ISSUES
Hardened grade of steel consist of a finer
microstructure
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Task 4.4.2 – Materials: Laboratory Testing G
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Corrosion on
sliding surface
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Task 4.4.2 – Materials: Laboratory Testing G
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Detail of contact area
Baseplate
1. Plint reciprocating rig
2. Bi-axial test rig
Example figure: Plint series one: VIM results
• lubricants G, R, and T performing
significantly better than the other
lubricants (M, and P) or than no
lubricant (D).
• In contaminated areas (e.g. coal),
lubricant G (and R) perform best
Conclusions
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T4.4.3 – Geometrical interface at crossings U
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Unsprung mass
Rail/Baseplate
Sleeper/Ballast
KPS CPS
KB
KS
CB
CS
Kcontact
Wheel + Crossing 3D
geometries RRD map and axle motion
Output dynamic impact
load in the wide
frequency range
Wheel CoG
vertical input
motion
3 d.o.f. unsrpung
mass-track model
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T4.4.3 – Geometrical interface performance U
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Graphical output of contact condition and contact stresses post-processing
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T4.4.3 – Geometrical interface performance
Performance of based on different wheel shapes
Flange thickness and increased cone angle (both nominal
radius and flange root) have a correlation with high dynamic
loads
False flange improves load transfer (lower depth of impact)
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Flange angle
(50mm from Flange face)
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T4.4.4 – Support stiffness
Use of resilient element such as USPs
Sylomer® UnderSleeper Pads
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(a) (b)
3D Finite Element
dynamic model
(POLIMI)
1 2 3 4 5 6 7-20
-15
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10Rail vert. bending moment (B633) - Test S3, V [km/h]=36
Time [s]
M [k
Nm]
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Speed
Peak
contact
force
Rail seat
force at slp
5
Sleeper-
ballast force
at slp 5
Bending
moment 2m
ahead of
crossing
Bending
moment 2m
ahead of
crossing
[km/h] [kN] [kN] [kN] [kNm] [kNm]
Reference S1 36 142.48 -43.47 75.91 -18.18 -16.58
Reference S1 120 145.69 -38.61 73.2 -17.76 -16.85
Soft rail pads S2 36 152.9 -49.6 80.08 -19.39 -17.35
Soft rail pads S2 120 157.8 -41.64 73.09 -18.47 -16.08
Under-sleeper pads S3 36 129.54 -39.47 71.93 -19.27 -16.99
Under-sleeper pads S3 120 129.53 -35.95 71.03 -19.05 -18.73
Ballast mat S5 36 141.62 -39.63 66.35 -19.34 -20
Ballast mat S5 120 152.16 -36.68 66.38 -19.34 -20.35
Turnout type Case
T4.4.4 – Support stiffness
Main outputs from simulation and measurements
Both site observation and simulation show improved performance
obtained from the use of USPs
Precise definition of USP stiffness/modulus is important to
achieve optimum performance (too soft might be damaging)
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y = 481,5x + 1239,3 R² = 0,7
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(µ
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Pad Type
D0 Deflection
D300 Deflection
D1000 Deflection
Linéaire (D0 Deflection)
Linéaire (D300 Deflection)
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Thank you
Contact: [email protected]
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More info in SUSTRAIL
Deliverable D4.4…
http://www.sustrail.eu/
Track based monitoring and limits for
imposed loads
Brussels, Belgium 21/05/15
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Track based monitoring and limits for imposed loads
Summary: Statistical information to employ reliability-based analysis,
Provide capability to reduce the uncertainty associated with critical parameters characterization; reflect their evolution (helpful for new designs and track optimization).
Lead to maintenance costs reduction without compromising safety
Partners Involved:
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Track based monitoring and limits for imposed loads
Task Highlights Identification of critical parameters to be monitored
State of the art in literature and in finding of other projects
Questionnaire about ALCs to IMs
Focus on track-based monitoring, inspecting forces on vehicles, imposed loads on the tracks
Inspection and monitoring technologies selection and description
Data analysis from Damill monitoring station
LTU, KTH and TRAIN modelling and analysis
UoH Dynamic Smart Washer prototype
Results are compiled in the report “Track based monitoring and limits for imposed loads” (SUSTRAIL Deliverable D4.5)
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Overview of Work Damill has developed a wayside
monitoring station named StratoForce.
The station is of type Axle load
Checkpoint (ALC) with both vertical and
lateral force measurement.
One such station is owned by Luleå
Railway Research Centre (JVTC).
That station has been used in Sustrail
for analysis of typical vehicle forces.
The data has been evaluated regarding:
Detectable defects.
Benefits of finding the defects.
Suggestions to service alarm limits.
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Wheel Force &
Steering Monitor
Sensors Mounted
Directly on Rail
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Detectable Defects in Axle Load Checkpoints
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Wheel out-of-roundness.
Wheel flat.
RCF surface defect.
Worn wheel profiles.
Suspension jamming.
Increased friction in bogie
centre bowl or side pads.
Skew loading of
wagon.
Broken suspension.
Skew/twisted wagon
frame.
Unstable operation
(hunting).
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Example of a Benefit if Defects are Found Early
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A possible reduction of track degradation could be achieved by over-hauling 5% of the axles generating top lateral forces.
The effect is a reduction of the average lateral forces by 10%.
A simple friction model indicate 10% reduction of wear on rails and wheels in curves.
Effect on RCF is not stated.
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0 50 100 150 200 250 300 350
L/V
left
ab
solu
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Axle load (kN)
Locomotives February
F140
RC
IORE
X62
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0,4
0,5
0,6
0,7
0,8
0 50 100 150 200 250 300 350
L/V
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Axle load (kN)
Wagons February
Fanoo loaded
SMMnps loaded
SJ coaches
Fanoo empty
SMMnps empty
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Proposals of Service Limits for Vehicles
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Yellow fields indicate new service limits added to the safety limits in the UIC HRMS report.
Lateral force limits may need local adjustment for each station.
Vehicle identification is necessary if alarm levels are to be vehicle dependent.
Parameter
Se
rvic
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Sa
fety
Vertical peak load [kN] T+C <200 <350
Skew load, Diagonal
quotient T <1,3 <1,7
Skew load, left/right
normally T <1,3 <1.7
Skew load
,longitudinal front/rear T+C <2 <3
Lateral/vertical wheel
load quotient Y/Q T+C
locos <0,5-0,7
wagons <0,4-0,5 <0,8
Dynamic/static wheel
load quotient T <0,4 <0,6
UIC HRMS report
Ta
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Track based monitoring and limits for imposed loads
Data Analysis of Heavy Haul Locomotive Wheel-sets’
Running Surface Wear Research Description:
Both an proposed integrated procedure for Bayesian reliability inference using Markov Chain
Monte Carlo (MCMC) and other traditional statistics theories (incl., reliability analysis, degradation
analysis, Accelerated Life Tests (ALT), Design of Experiments (DOE)) are applied to a number of
case studies using heavy haul locomotive wheel-sets’ running surfaces wearing data from Iron Ore
Line (Malmbanan), Sweden.
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The research explores the impact of the locomotive wheel-sets’ installed
position (incl. positions of the installed locomotive, bogie, axel.) on their
service lifetime and attempts to predict the reliability related
characteristics.
Results from this research will support locomotive wheels’ maintenance
strategies using data analysis of wheels’ running surface wear.
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Track based monitoring and limits for imposed loads
Results An Integrated Procedure for Wheel-sets surface wearing analysis using Bayesian Reliability
Inference via MCMC;
parametric Bayesian models (including Bayesian Exponential Regression Model, Bayesian
Weibull Regression Model, and Log-normal Regression Model, etc.), non-parametric Bayesian
models (piecewise constant hazard rate, etc.), frailty models (gamma frailty, etc), as well as the
comparison studies for wheel-sets surface wearing;
other traditional statistical approaches (incl., reliability analysis, degradation analysis, Accelerated
Life Tests (ALT), Design of Experiments (DOE)) for exploring the impact of the locomotive wheel-
sets’ installed position.
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Track based monitoring and limits for imposed loads
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Main Conclusions the wheel-sets’ lifetimes differ according to where they are installed on the locomotive. The
differences could be influenced by such factors as the operating environment (e.g., climate,
topography, track geometry), configuration of the suspension, status of the bogies and spring
systems, operating speeds and applied loads, as well as human influences (drivers’ operations,
maintenance policies, lathe operators etc.);
rolling contact fatigue (RCF) is the main type of re-profiling work order;
the re-profiling parameters can be applied to monitor both the wear rate and the re-profiling loss;
the total wear of the wheels can be determined by investigating natural wear and/or loss of wheel
diameter through re-profiling loss, but these differ across locomotives and under different
operating conditions;
the bogie in which a wheel is installed is a key factor in assessing the wheel’s reliability.
The best life distribution is a 3-parameter Weibull distribution;
Comparing the wear data of the wheel-sets’ running surfaces (including total wear rate, natural
wear rate, re-profiling wear rate, the ratio of re-profiling and natural wear) is an effective way to
optimise maintenance strategies;
More natural wear occurs for the wheels installed in axel 1 and axel 3, a finding that supports
related studies at Malmbanan.
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Condition monitoring methods employed in other industries which have potential applications in rail industry
Method 1 - gearbox fault detection using inverter signals to monitor the influence of the mechanical load and detect the electrical and electromechanical faults on an inverter-driven motor system.
Comparison between the measured AC motor currents
Method 2 - modified bispectrum analysis of the stator
current can be used in association with the kurtosis value of
the raw current signal for reliable fault classification results.
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The piezo-resistive based clamping force sensor (packaged as a smart washer) was developed by using a combination of fragile piezo-resistive sensor elements, elastomers and polymeric material that can resist fluctuations in the environment yet withstand significant loads, applied for long periods of time.
Practical lab tests have shown a non-linear relationship between the sensor resistivity and the axial load over the range 20 to 70 kN for compression (bolt tightening) and decompression (bolt slackening).
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Digital response curve of the
washer prototype as a function
of applied axial force
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Critical parameters assessment for track load evaluation
Survey about existing ALCs - Collation relevant data from IMs
Identification of defects detectable with ALC
Identification and development of technologies to monitor track and structure in order to optimize preventive and intervention level maintenance strategies
Simulation and computation performed for range of imposed loads and other parameters
Proposals of Service Limits for Vehicles
Support locomotive wheels’ maintenance strategies using data analysis of wheels’ running surface wear
Smart washer prototype tested in-Lab
Time interval maintenance Condition based preventive maintenance
For Network operators
Provide some tools to identify violator vehicles so that appropriate action
can be agreed with the operators.
For Train operators
Monitor the condition of individual vehicles over time
Schedule preventative maintenance to achieve longer life and decrease LCC.
Track based monitoring: Conclusion
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WP4 Sustainable Track
Discussion / Questions
Sustainable Track Presenters:-
Clemente Fuggini (TRAIN)
Donato Zangani (TRAIN)
Yann Bezin (HUD)
Francois Defossez (MERMEC)
Kevin Blacktop (NR)
WP 4: Sustainable Track