Commercial in ConfidenceWayside Monitoring ofRolling Stock ConditionState of Art for Heavy Haul RailwaysDavid RennisonMana...
Commercial in ConfidenceContents• Why Wayside Monitoring ??• Technologies & their challenges– Wheel Profile & Component Im...
Commercial in ConfidenceAsset life reduction at the extreme
Commercial in ConfidenceWayside Monitoring ??Monitoring the condition of rolling stock leads to:• Increased safety & reduc...
Commercial in ConfidenceWheelsets command a high % of the wagonmaintenance budgetWheel tread BogieWheelsetWheel bearing
Commercial in ConfidencePriorities in Condition Monitoring• 20% of your wheelsets would most likely have maintenance each ...
Commercial in ConfidenceWayside Monitoring Basics• Wayside Sensor Systems:– Directly measure key properties (or indicators...
Commercial in ConfidenceMonitoring Sensor Subsystems can include:BAM WCM Wheel Profile:
Commercial in ConfidenceVideoImagingWIMMonitoring Sensor Subsystems can include:BAM DED WID HABD:
Commercial in Confidence
Commercial in ConfidenceWayside Monitoring Basics cont’d• Sensors must function in robust environmental conditions– Can sp...
Commercial in Confidence“SuperSite” - Interaction ModelWheel SurfaceDefectsBearingConditionWheel Profile&WearBogie Geometr...
Commercial in ConfidenceWheel Profile & Component Imaging
Commercial in Confidence
Commercial in ConfidenceBrake Shoe ImagingJoined Images &DimensioningRaw Images
Commercial in ConfidenceWheel Profile & Component Imaging• Primary outputs– Wheel Profile Parameters– Component dimensions...
Commercial in ConfidenceBogie Geometry & Vehicle Hunting• Angle of Attack • Tracking PositionGeometry Definitions
Commercial in ConfidenceBogie Geometry & Vehicle HuntingOptical Unit (laser,camera, etc.)Wheel Sensors(speed, location)
Commercial in ConfidenceRepeatability of Optical Results-4-20246813 14 15 16 17 18 19 20 21 22 23 24axle #angle,mrad-60-45...
Commercial in ConfidenceLaterally Unstable AxlePosition 1 Position 2 Position 3
Commercial in Confidence“High frequency” instabilityTrain 2002-09-02-07-29; v = 76 km/h, a = 21.9 mm, f = 2.7 hz-30-20-100...
Commercial in ConfidenceBearing Defect Detection• Package Bearing Unit, typical of use in freight vehicles• Held on axle b...
Commercial in Confidence‘Common’ Bearing Defects
Commercial in ConfidenceBearing Defect Detection• Hot & warm bearings captured using thermal imagingsensors (HABD’s, HWD’s...
Commercial in ConfidenceBearing Defect Detection
Commercial in ConfidenceDefect Severity ProgressionLevel 3 / LowSeverity DefectLevel 1 / HighSeverity DefectLevel 2 / Medi...
Commercial in ConfidenceDegrading of Bearing Fault identified by BAM
Commercial in ConfidenceHeavy Haul Benefits
Commercial in ConfidenceBearing Defect Detection Summary• Acoustic bearing defect detectors used forpredictive maintenance...
Commercial in ConfidenceWheel Defect DetectionShelling
Commercial in ConfidenceWheel Defect Detection• As defect grows, so associatedimpact forces grow.• Tread defect always gro...
Commercial in ConfidenceWheel Defect sensor systems have progressedStrain gauge based WILD used initially - most common bu...
Commercial in ConfidenceWheel Condition Monitor
Commercial in ConfidenceData Integration• Integration of (reliable) data into (routine) maintenanceplanning and Train Cont...
Commercial in Confidence
Commercial in ConfidenceSummary Points• Benefits Realized ?– Number of unplanned stoppages has reduced significantly– Safe...
Commercial in ConfidenceObrigado
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Wayside monitoring of vehicle condition - current state of art for heavy haul railways

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Dr. David Rennison, Managing Director, from Trackside intelligence, Australia has presented at the Heavy Haul Rail South America. If you would like more information about the conference, please visit the website: http://www.railconferences.com/heavyhaulrail/southamerica

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

  1. 1. Commercial in ConfidenceWayside Monitoring ofRolling Stock ConditionState of Art for Heavy Haul RailwaysDavid RennisonManaging DirectorTrackside Intelligence Pty Ltdwww.trackiq.com.au
  2. 2. Commercial in ConfidenceContents• Why Wayside Monitoring ??• Technologies & their challenges– Wheel Profile & Component Imaging– Bogie geometry– Bearings– Wheels– Data Integration• Benefits realized ?• Goals Revisited
  3. 3. Commercial in ConfidenceAsset life reduction at the extreme
  4. 4. Commercial in ConfidenceWayside Monitoring ??Monitoring the condition of rolling stock leads to:• Increased safety & reduced risk• Reduced unplanned stoppages & improved assetavailability• 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
  5. 5. Commercial in ConfidenceWheelsets command a high % of the wagonmaintenance budgetWheel tread BogieWheelsetWheel bearing
  6. 6. Commercial in ConfidencePriorities 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% UnclassifiedWheel ImpactBogie GeometryWheel ProfileAcoustic Monitoring
  7. 7. Commercial in ConfidenceWayside Monitoring Basics• Wayside Sensor Systems:– Directly measure key properties (or indicators) ofphysical defects of operational vehicles– Aspire to deliver a “rich” stream of high qualitydata– Integration within wagon ID & train information– Often co-located at Asset Protection Supersites
  8. 8. Commercial in ConfidenceMonitoring Sensor Subsystems can include:BAM WCM Wheel Profile:
  9. 9. Commercial in ConfidenceVideoImagingWIMMonitoring Sensor Subsystems can include:BAM DED WID HABD:
  10. 10. Commercial in Confidence
  11. 11. Commercial in ConfidenceWayside Monitoring Basics cont’d• Sensors must function in robust environmental conditions– Can span from -40oC to 60oG plus solar load, Hot / wet; highvibration, 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 inputsfrom multiple sensors– Issue Alerts, messages, reports & maintenance planninginformation
  12. 12. Commercial in Confidence“SuperSite” - Interaction ModelWheel SurfaceDefectsBearingConditionWheel Profile&WearBogie Geometry& HuntingAoA, TP, …Safety&Economics
  13. 13. Commercial in ConfidenceWheel Profile & Component Imaging
  14. 14. Commercial in Confidence
  15. 15. Commercial in ConfidenceBrake Shoe ImagingJoined Images &DimensioningRaw Images
  16. 16. Commercial in ConfidenceWheel 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 peakacceleration; ingress of water, dust, grease)
  17. 17. Commercial in ConfidenceBogie Geometry & Vehicle Hunting• Angle of Attack • Tracking PositionGeometry Definitions
  18. 18. Commercial in ConfidenceBogie Geometry & Vehicle HuntingOptical Unit (laser,camera, etc.)Wheel Sensors(speed, location)
  19. 19. Commercial in ConfidenceRepeatability of Optical Results-4-20246813 14 15 16 17 18 19 20 21 22 23 24axle #angle,mrad-60-45-30-1501530position,mmangle 04 Sept angle 06 Sept angle 09 Sept angle 11 Sept angle 17 Sept angle 18 Septpos 04 Sept pos 06 Sept pos 09 Sept pos 11 Sept pos 17 Sept pos 18 SeptNote: Speed varied in the range 65 – 204 km/hSix Passes of Train on Straight Track, over 14 days
  20. 20. Commercial in ConfidenceLaterally Unstable AxlePosition 1 Position 2 Position 3
  21. 21. Commercial in Confidence“High frequency” instabilityTrain 2002-09-02-07-29; v = 76 km/h, a = 21.9 mm, f = 2.7 hz-30-20-100102030191 193 195 197 199 201 203 205 207 209axle #tr.position,mmP1 P2 P348 mph
  22. 22. Commercial in ConfidenceBearing 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
  23. 23. Commercial in Confidence‘Common’ Bearing Defects
  24. 24. Commercial in ConfidenceBearing Defect Detection• Hot & warm bearings captured using thermal imagingsensors (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 wheelnoise and other operational noise
  25. 25. Commercial in ConfidenceBearing Defect Detection
  26. 26. Commercial in ConfidenceDefect Severity ProgressionLevel 3 / LowSeverity DefectLevel 1 / HighSeverity DefectLevel 2 / MediumSeverity DefectNov 2012Commercial In Confidence 27
  27. 27. Commercial in ConfidenceDegrading of Bearing Fault identified by BAM
  28. 28. Commercial in ConfidenceHeavy Haul Benefits
  29. 29. Commercial in ConfidenceBearing Defect Detection Summary• Acoustic bearing defect detectors used forpredictive maintenance purposes– Siemens are pushing bearing inspections from 600,000 miles to1.4 million miles using RailBAM technologies in regionalpassenger 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 toreduce costs• Challenges:– Reliable detection on single passby.– Extension to locomotives, inboard bearings
  30. 30. Commercial in ConfidenceWheel Defect DetectionShelling
  31. 31. Commercial in ConfidenceWheel Defect Detection• As defect grows, so associatedimpact forces grow.• Tread defect always grows insize.
  32. 32. Commercial in ConfidenceWheel Defect sensor systems have progressedStrain gauge based WILD used initially - most common but limited bysignal processing method. Targeted at “Wheel Flats”• Peak forces & quasi-static wheel weights, reported.Fibre-optic sensor array clamped below the rail - measure raildisplacement and infer wheel forces.• Peak forces & quasi-static wheel weights, reportedOptical scanning of wheel circumference• Prototype systems in developmentAccelerometer / 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
  33. 33. Commercial in ConfidenceWheel Condition Monitor
  34. 34. Commercial in ConfidenceData Integration• Integration of (reliable) data into (routine) maintenanceplanning 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 & distributeinformation to engineering, maintenance, train control– Integrate with MMS (SAP, Maximo, etc.) with maintenance history
  35. 35. Commercial in Confidence
  36. 36. Commercial in ConfidenceSummary Points• Benefits Realized ?– Number of unplanned stoppages has reduced significantly– Safety improved and railway capacities increased– Maintenance productivity increased by closer targeting of criticalvehicles• Goals Revisited:– Likely remain the same but with more pressure on sensorperformance.– Implementation will be refined to improve effectiveness andextract more information• Challenges:– Improving relationships between indirect measurements andphysical defects (wheel & bearing defects, bogie geometry)– High reliability rule sets for automation of maintenance planning
  37. 37. Commercial in ConfidenceObrigado
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