wayside monitoring of vehicle condition - current state of art for heavy haul railways
TRANSCRIPT
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
Commercial in Confidence
Contents
• Why Wayside Monitoring ?? • Technologies & their challenges
– Wheel Profile & Component Imaging – Bogie geometry – Bearings – Wheels – Data Integration
• Benefits realized ? • Goals Revisited
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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
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Wheelsets command a high % of the wagon maintenance budget
Wheel tread Bogie Wheelset Wheel bearing
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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
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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
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Video Imaging WIM
Monitoring Sensor Subsystems can include: BAM DED WID HABD :
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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
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“SuperSite” - Interaction Model
Wheel Surface Defects
Bearing Condition
Wheel Profile &
Wear
Bogie Geometry & Hunting AoA, TP, …
Safety &
Economics
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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)
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Bogie Geometry & Vehicle Hunting
• Angle of Attack
• Tracking Position
Geometry Definitions
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Bogie Geometry & Vehicle Hunting
Optical Unit (laser, camera, etc.)
Wheel Sensors (speed, location)
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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
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“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
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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
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Bearing Defect Detection
• 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
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Defect Severity Progression
Level 3 / Low Severity Defect
Level 1 / High Severity Defect
Level 2 / Medium Severity Defect
Nov 2012 Commercial In Confidence 27
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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
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Wheel Defect Detection
• As defect grows, so associated impact forces grow.
• Tread defect always grows in size.
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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
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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
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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