Collecting and exploiting trainborne
data
Kevin Hope CEng FIMechE FPWI
Contents:
1. A brief history of asset data collection
2. Recent(ish) developments
3. Predict & Prevent
Rail accident at Hatfield
• 17
thOctober 2000
• 4 people lost their life
• Broken rail caused derailment
• Rolling Contact Fatigue
Ultrasonic Test Units (UTUs)
Operation
2 man team
~240 shifts
~19,000 miles per annum.
Night time recording @ 30mph
Frequency - 8,16 or 24 weekly
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What breaks rails?
• For the purposes of analysis broken rails have been subdivided into 5 simple categories;
• Head Inherent – covering all breaks from defects in the head of the rail such as squats, Tache Ovales, Transverse defects from RCF
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What breaks rails? (continued)
• Welds – covering all breaks at welds including alumino-thermic, flash butt and weld repairs
• Rail Ends / Joints – covering breaks from bolt holes within fishplated joints and breaks along the upper or lower fillet radii at rail ends
The impact of UTU introduction
952 919 706 534 445 500 600 700 800 900 1000 1100 N u m b e r o f B re a ksOFFICIAL
Rail Foot Failures +14%
2010-11 67 out of a total of 171 2015-16 51 out of a total of 94
Welds -15%
2010-11 50 out of a total of 171
2015-16 18 out of a total of 109
What Breaks 2010-11 to 2015-16 - What’s Changed?
67
27
7
50
Additional UTU equipment
UTUs also fitted with:
• Ground Penetrating Radar
• Rail Profile
New Measurement Train
Operation
Commenced service June 2003
3 man team
350 shifts
Frequency - 4 weekly
80,000 miles per annum
Outputs
Track geometry
Track interval spacing
Forward facing HD Video
Video Inspection (PLPR)
OLE non-contact system
OLE wire wear
Gauging
Structure Gauging –
clearance to structures
Track interval - clearance to
passing vehicles
Overhead Line - Mentor
Operation
2 man team
80 shifts
15000 miles per annum
Frequency – twice per year
Outputs
Contact force
Wire height
Wire stagger
Pantograph accelerations
Line voltage
Video inspection – Plain Line Pattern Recognition
• Plain Line Pattern Recognition
• Inspect plain line CWR rail
• Replaces basic visual
inspection (BVI)
PLPR data gathering
• On a Network Rail track recording
vehicle
• 7 under train cameras to capture images
• 4 lasers used to build up 3 dimensional
profile of an object
• Ballast shoulder lasers to determine
ballast profile
PLPR inspection and reporting
• Examination Inspectors (EI’s)
verify candidates and fault
any defects they have seen
on the screen.
• Select correct fault details
• Take pictures to help explain
the fault.
• Each defect is signed with a
time and date.
Rail Surface Crack – Eddy Current
An AC current is passed through the coil which produces a magnetic field around the coil
If a flaw such as a RCF crack disturbs the eddy currents the magnetic
coupling with the material is altered and the signal can be read by
measuring the change in impedance across the coil
When the coil is placed near a conducting material it induces eddy currents in the surface of the material
Trainborne inspection process
Data
Collection
Data processing
Maintenance
Fault Management
Defects
Database &
Reports
Why is reactive maintenance a problem?
But Service Affecting Failures have reduced… Performance is currently operating below target
The impact of each Failure is increasing
Embedded monitoring summary
14,449 Points (69%) 3,241 Point heating supplies (100%) 1,422 Power supplies (100%) 24,449 Track circuits (45%) Rail and equipment room tempPoints condition monitoring example
Backdrive tight
Dry slidechairs
Green Colour Used Normal/Healthy Condition Green Colour Used Normal/Healthy ConditionOFFICIAL
Data architecture
Analytics Platform
Enterprise Asset Management System
Asset Manager Asset Engineers Examiner / Maintainer IP / Supply Chain Professional Head
Enterprise Asset Management System Enterprise Asset Management Systems
Aerial Survey
Train-Borne Data Collection
Measurement Fleet and Service Trains
Remote Condition Monitoring
Condition Sensors, Data Loggers,, SCADA Systems, etc Aerial Imagery, LIDAR, Digital Terrain Model, etc
DASHBOARDS AND FRONT-END USER TOOLS
ANALYTICAL MODELS AND ALGORITHMS
ANALYTICS WAREHOUSE AND CONDITION STORE
Other Data Sources
Resource Management Systems SURVEY / MEDIA PLATFORM REFERENCE ARCHITECTURE
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Process activities
ate of degradation
1. Receive Data 2. Basic Data Validation 3. Map to Network Model 4. Linear Run-On-Run Alignment 5. Enhanced Data Validation 7. Analyse Data (e.g. Identify Defect) Identify Missed Runs / Section Report Compliance Create Manual Inspections 8. Present Results and Supporting Data 10. Create / Update Defects / Work / MST 6. Perform Data Transformation (e.g. video -> condition)
COMMON DATA STORAGE (E.G. DATA LAKE, AZURE SQL DB)
Data is received from the train and persisted to a common data store Basic validation is performed e.g. to check file format, etc Raw data is mapped to the Network Model to establish the path the condition data relates to Raw data is mapped to the Network Model to establish the path the condition data relates to Data is analysed (manual or hopefully automated) to mark any invalid recordings If needed, data is converted from one format to another (e.g. video converted to condition) Data is analysed to identify defects, and predict time to failure based on condition trends Information is presented to users in format that helps them make a decision, plus also supports them taking action Transactions created in EAM system to respond to the findings of the analysis / decision being made
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Exploiting the data – Decision Support Tools (DSTs)
– Rate
of degradation
The eTDST algorithm aligns trace data from the Track Geometry Measurement Trains to provide maintenance teams with run-on-run trace that can be used to analyse and demonstrate the deterioration of an asset over time.Users are able to use the eTDST on a mobile device with just a 3G connection, meaning they can access this data when they need it wherever they are.
Track location selected in Risk Site Table
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Predicting time to failure
ease A – Rate of degradation
Users are able to examine the rate of degradation for a specific location over time. This chart provides the current rate of degradation of a track asset along with the prediction of when it will hit either an AL or IL level, based on linear regression.
This graph demonstrates not only any track degradation, but also begins to demonstrate the effectiveness of any work carried out at a specific location..
Specific location selected in Run on Run Trace
Looking at all data the algorithm predicts location will reach AL by 22/06/19
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Prioritising intervention
– Risk Sites Dashboard
Users see a list of current Track Geometry risk sites that have been identified by the data extracted from the Track Geometry
Measurement Trains. Data is given on the location of the risk site, type of Track Geometry Parameter, asset information and time until the identified defect is predicted to hit Alert Limit (AL) & Intervention Limit (IL).
Filtering information by TG Parameters, ELRs, Track ID, Start & End Mileage, Sleeper Type and Track Category allows users to target the type of information they see in their risk sites page, making it easier to focus on specific work.
Risk site selected to investigate
Options users can filter by to
Users are able to export risk sites and data from the DST
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Track DST development plan
– Risk Sites Dashboard
Work Package Description Target Reduction Work
Package A
• Track Geometry & Cyclic Top Defect Review Page
• PLPR Defect Review Page
• Maintenance Dashboard & Landing Page
• Intervention Support & Planning Page
• Track Quality & Trace Review Page
Work Package B
• Rail Management Defect Review Page:
• Rolling Contact Fatigue
• Rail Flaws • Side Wear • Rail Depth • IRJs • Ballast Profile Work Package C • Structure Gauging
• Supervisor’s Inspection App
• Rail Stress Data - Analytics & Mobile Inspection App
Work Package D
• Enhanced Criticality Models
• Remote Intervention Planning
• Expedited Rough Ride Reports
• Renewal Effectiveness Data
Work
• S&C Analytics Functionality & Video Measurement Data
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System criticality – impact on the timetable
– Risk Sites
Dashboard
Prioritisation based on the impact of the timetable is key to Putting Passengers First – what should we prioritise to minimise delays? Even if a particular asset is predicted to fail first, it may not be top priority – the impact on the timetable of another asset failing might be greater.OFFICIAL
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… for Advisory Generation (rail treatment)
Sites
Dashboard
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In n o v a ti o n is i n o u r n a tu reTrack, Maintenance and Plant Overview
Track, Maintenance and Plant
Track S&C Modular Bearer Joint redevelopment HPSS development and redeployment Improvement of weld repair process for S&C
(2.3) S&C Laser Robotic inspection S&C Higher Turnout Speeds CAST Crossings Plain Line Track Track Ex development (2.3) Induction Welding Optimised Track Formation Treatments (2.2) Preventing rail squats (2.3) TSR/ESR Board Design and Positioning
Resilient rail track systems (2.1)
High speed Rail
profiling RODIO FOAS & IoT
LoWRAN HABD Remote Condition Monitoring S&C Measurement train Embedded Monitoring Enhanced Rail Flaw detection (2.1) Unattended Measurement Alternative Gauging Methodology System Model and Asset Criticality (2.1) Predicting Failures & Degradation Trainborne Asset Monitoring Dynamic Track Stiffness Maintenance Optimising Maintenance Long-Term Workbank and RAMP Tool (2.3) Work Delivery Planning (2.3) Improved Relationship Models Plant Virtual Reality Training Robotic Inspection and Maintenance Vehicle RRV Crane Runaway Break Development Autonomous Plant Portable OTP Track Access
Priority 2 Projects (sub-priority in ())
Completed Projects
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Driver Reported Rough Rides
Signaler informs incident control Line blockage or speed restriction On-site inspection: Linespeed operation re-instated by competent person
• A Driver Reported Rough Ride occurs when the train driver
or the crew feels a movement that they believe could pose
a risk to the safe passage of trains
• Designed to detect instantaneous or rapidly developing
issues e.g. embankment failure, broken rail, track buckle
• Current Track Geometry regime isn’t frequent enough to
detect these types of event
• General assumption is that there must be something in the
track that is causing the problem…
• However, based on 43 Driver Reported Rough Rides on
one track section in 2018:
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Track Geometry & Rough Rides
• Track Geometry faults are not a good indicator of
where Rough Rides will be reported
• Poor Track Quality is also not a good indicator of
where Rough Rides will be reported
• Driver Reported Rough Rides are a ‘measure’ of how
a vehicle has responded to the track. Lots of
variables:
• Vehicle type, speed and condition
• Track features and combinations of features
• Weather/trackbed conditions, noise
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In n o v a ti o n is i n o u r n a tu reReliable and Sustainable Power Overview
P1 or P2 ‘in flight’
Reliable &
Sustainable Power
Traction &
Rolling Stock
Distribution
Contact
Systems
Strategy
EMC Feasibility for New Train Introductions
UPS Condition Monitoring
Circuit Breaker Trip Monitoring Signalling Power Supply
Quality Monitoring Capability DC Trackside Energy Storage HV Cable Discharge Monitoring
Automated Isolation & Earthing
Level Crossing Power Supplies Uninterruptable Power Supply Vendor Agnostic
Remote Monitoring Optimum Traction Energy Cost Efficient Electrification Cost Efficient Electrification Ph2
Electric Traction Supply Innovations Shoegear Conductor Rail
Interface Monitoring System (SRIMS) Advanced inspection
methods for contact wire cracking Inline Condition Monitoring of OLE System Uplift / Line
Tension
OLE Dynamic Modelling
OLE Pattern Recognition Inline Neutral Sections
