Wayside monitoring of vehicle condition - current state of art for heavy haul railways

Commercial in Confidence

Wayside Monitoring of Rolling Stock Condition

State of Art for Heavy Haul Railways

David Rennison Managing Director Trackside Intelligence Pty Ltd www.trackiq.com.au

http://www.trackiq.com.au/


Contents

• Why Wayside Monitoring ?? • Technologies & their challenges

– Wheel Profile & Component Imaging – Bogie geometry – Bearings – Wheels – Data Integration

• Benefits realized ? • Goals Revisited


Asset life reduction at the extreme


Wayside Monitoring ??

Monitoring the condition of rolling stock leads to: • Increased safety & reduced risk • Reduced unplanned stoppages & improved asset

availability • Reduced maintenance costs

• For rollingstock and rail infrastructure • Identification of systemic problems in maintenance processes • Optimized maintenance intervention

• Reduced environmental impact • More knowledgeable & pro-active staff

• Both Pro-active and Reactive components


Wheelsets command a high % of the wagon maintenance budget

Wheel tread Bogie Wheelset Wheel bearing


Priorities in Condition Monitoring

• 20% of your wheelsets would most likely have maintenance each year

• Of these: • 26% may have Tread Defects

» Impact » Spalling » Shells » Skids

• 23% may have Thin Flanges » Pulling to one side

• 17% normal wear » Hollowing » Hi Flanges

• 13% Bearing Defects » Running Surface » Looseness Fretting » Leaking grease / Seals

• 21% Unclassified

Wheel Impact

Bogie Geometry

Wheel Profile

Acoustic Monitoring


Wayside Monitoring Basics

• Wayside Sensor Systems: – Directly measure key properties (or indicators) of

physical defects of operational vehicles

– Aspire to deliver a “rich” stream of high quality data

– Integration within wagon ID & train information

– Often co-located at Asset Protection Supersites


Monitoring Sensor Subsystems can include: BAM WCM Wheel Profile :


Video Imaging WIM

Monitoring Sensor Subsystems can include: BAM DED WID HABD :



Wayside Monitoring Basics cont’d

• Sensors must function in robust environmental conditions – Can span from -40oC to 60oG plus solar load, Hot / wet; high

vibration, often exceeding EN standards.

• Alerts, based on data trends – Traditionally Alarms (to stop the train)

• Databases: – Provide data storage for wide range of sensors – Filter data through series of “customizable” rules based on inputs

from multiple sensors – Issue Alerts, messages, reports & maintenance planning

information


“SuperSite” - Interaction Model

Wheel Surface Defects

Bearing Condition

Wheel Profile &

Wear

Bogie Geometry & Hunting AoA, TP, …

Safety &

Economics


Wheel Profile & Component Imaging



Brake Shoe Imaging

Joined Images & Dimensioning

Raw Images


Wheel Profile & Component Imaging

• Primary outputs – Wheel Profile Parameters – Component dimensions

• Derived outputs – Wheel Profile matching to rail profile – Differential wear between wheels on same axle – Wear rates and prediction of exceedance of allowable limits

• Challenges – Environmental conditions (-40oC to 60oC; 1000g’s peak

acceleration; ingress of water, dust, grease)


Bogie Geometry & Vehicle Hunting

• Angle of Attack

• Tracking Position

Geometry Definitions


Bogie Geometry & Vehicle Hunting

Optical Unit (laser, camera, etc.)

Wheel Sensors (speed, location)


Repeatability of Optical Results

-4

-2

0

2

4

6

8

13 14 15 16 17 18 19 20 21 22 23 24

axle #

angle, mrad

-60

-45

-30

-15

0

15

30

position, mm

angle 04 Sept angle 06 Sept angle 09 Sept angle 11 Sept angle 17 Sept angle 18 Sept

pos 04 Sept pos 06 Sept pos 09 Sept pos 11 Sept pos 17 Sept pos 18 Sept

Note: Speed varied in the range 65 – 204 km/h

Six Passes of Train on Straight Track, over 14 days


Laterally Unstable Axle

Position 1 Position 2 Position 3


“High frequency” instability

Train 2002-09-02-07-29; v = 76 km/h, a = 21.9 mm, f = 2.7 hz

-30

-20

-10

0

10

20

30

191 193 195 197 199 201 203 205 207 209axle #

tr. position,

mm

P1 P2 P3

48 mph


Bearing Defect Detection

• Package Bearing Unit, typical of use in freight vehicles • Held on axle by press fit and 3 bolts as shown. • Supported under Adaptor within bogie side frame


‘Common’ Bearing Defects



• Hot & warm bearings captured using thermal imaging sensors (HABD’s, HWD’s) – Reactive, last resort

• Developing defects measured using acoustic methods (ABD’s). Want to know: – Defect size & location – Axial wear & fretting – Presence of worn cage slots – Loose cones

• Derived from acoustic signature in presence of wheel noise and other operational noise




Defect Severity Progression

Level 3 / Low Severity Defect

Level 1 / High Severity Defect

Level 2 / Medium Severity Defect

Nov 2012 Commercial In Confidence 27


Degrading of Bearing Fault identified by BAM


Heavy Haul Benefits


Bearing Defect Detection Summary

• Acoustic bearing defect detectors used for predictive maintenance purposes – Siemens are pushing bearing inspections from 600,000 miles to

1.4 million miles using RailBAM technologies in regional passenger train application

• HABD’s used as fallback sensor systems – Temperatures trended and compared to consist average values – For low density networks, refine spacing to critical locations to

reduce costs

• Challenges: – Reliable detection on single passby. – Extension to locomotives, inboard bearings


Wheel Defect Detection

Shelling


Wheel Defect Detection

• As defect grows, so associated impact forces grow.

• Tread defect always grows in size.


Wheel Defect sensor systems have progressed

Strain gauge based WILD used initially - most common but limited by signal processing method. Targeted at “Wheel Flats”

• Peak forces & quasi-static wheel weights, reported.

Fibre-optic sensor array clamped below the rail - measure rail displacement and infer wheel forces.

• Peak forces & quasi-static wheel weights, reported

Optical scanning of wheel circumference • Prototype systems in development

Accelerometer / load cell contiguous array clamped below the rail – measure wheel forces from calibrated accelerometers

• Peak forces, quasi-static wheel weights, defect size, reported • Circumferential wheel roughness around 5 wheel segments, reported • Circumferential Shelling Level, reported


Wheel Condition Monitor


Data Integration

• Integration of (reliable) data into (routine) maintenance planning and Train Control

• Most suppliers provide proprietary data database, presentation and alarming software for their sensors

• A few integration products available (InteRRIS, GWDS, WMS, NEMA)

• New generation products – Users require more flexibility in data access & hold own data – Integrate all wayside sensor data streams with data checkers – Flexible graphical display of data to suit individual customers – Extensive search engines to allow customer flexibility & distribute

information to engineering, maintenance, train control – Integrate with MMS (SAP, Maximo, etc.) with maintenance history



Summary Points

• Benefits Realized ? – Number of unplanned stoppages has reduced significantly – Safety improved and railway capacities increased – Maintenance productivity increased by closer targeting of critical

vehicles

• Goals Revisited: – Likely remain the same but with more pressure on sensor

performance. – Implementation will be refined to improve effectiveness and

extract more information

• Challenges: – Improving relationships between indirect measurements and

physical defects (wheel & bearing defects, bogie geometry) – High reliability rule sets for automation of maintenance planning


Obrigado

Date post:	20-Aug-2015
Category:	Business
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Wayside monitoring of vehicle condition - current state of art for heavy haul railways

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