sustrail - wp4 sustainable track final conference meeting · comparing the wear data of the...

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)


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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WP 4: Sustainable Track

WP4 Deliverables

The WP4 Deliverables have all been submitted

SUSTRAIL

Sustainable Track towards a “zero”

maintenance track

Performance based design principles

Clemente Fuggini,

[email protected]

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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


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Moving towards Performance Based Design Principles

Deterministic Probabilistic

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Content of

information Accuracy

Deterministic

Probabilistic

Δperformance

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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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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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This imply introducing the concept of reliability, but how?

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14 Performance based design principles

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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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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 - 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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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 - 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


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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


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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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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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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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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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Failures priorities code

Failure

Impact/Performance Failure Management

Cost

Analysis

Cost Impact

on Risk

FIC

Sev

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)

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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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From priorities to selection of where improvements are needed (“innovations”)

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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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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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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


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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


V=140 km/h

Axle load=22.5t

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Influence of Speed/Axle load


V=140 km/h

Axle load=22.5t


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

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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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



bearers fatigue cracking, voids poor support condition and maintenance, high track dynamic interaction

switch rails lipping, head checks, squats, wear



points all the above + fracture by fatigue as above + poor connection to stock rail or obstruction

stock rails lipping, head checks, squats, wear, spalling



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

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Eq

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tsS

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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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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

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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

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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

C:/Users/sengyb/Dropbox/Work/EU_FP7_SUSTRAIL/meeting_2015_April_UKdisseminationEvent_MKeynes_NR/animation_CEN56E1_9-25_halfcant_V80_y0_S0.mp4

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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

-10

-5

0

5

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

0

500

1000

1500

2000

2500

3000

0 0,5 1 1,5 2

Defl

ecti

on

(µ

m)

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/

mailto:[email protected]



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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Subject 67

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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

te v

alu

e

Axle load (kN)

Locomotives February

F140

RC

IORE

X62

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

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left

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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

e

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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en

t o

r

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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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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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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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Discussion / Questions

Sustainable Track Presenters:-

Clemente Fuggini (TRAIN)

Donato Zangani (TRAIN)

Yann Bezin (HUD)

Francois Defossez (MERMEC)

Kevin Blacktop (NR)

WP 4: Sustainable Track

sustrail - wp4 sustainable track final conference meeting · comparing the wear data of the...

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