Engineers Ireland - Irish Rail - Technological Advances in Heavy Rail 28th March 2018
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Technological Advances in Heavy Rail 28th March 2018 Engineers Ireland - Irish Rail Peter Smyth Chief Mechanical Engineer Irish Rail
Technological Advances in Heavy Rail - Irish Rail I will talk about: • Technology, innovation and systems in use on Irish Rail and on the global Railway. • Some of the latest innovations and ideas to optimise and improve maintenance. • I will also look forward to the future to consider what might be - principally from a rolling stock operational and maintenance perspective
Irish Rail CME – Key Numbers
500 Staff
80 €m Total Maintenance p.a
9 Depot Locations
30 €m Diesel p.a
800 New wheelsets p.a
200 Bogies p.a
Operating safe, customer-focused, sustainable services
across Intercity, DART and Commuter which benefit
our economy, communities and our customers
Our Fleets
• Fleets from Japan, Korea, Spain, USA,
Germany, France
• EMU (Dublin)
• DMU (Commuter / Intercity)
• Locomotives & carriages – traditional train
• A peak time railway
Fleet Disposition
TYPE CLASS BUILDER COUNTRY DATE SETS VEHICLES COMMENT IN SERVICE TOTAL
DMU 29000 CAF Spain 2003-2005 29 116 116
2600 Tokyu Car Japan 1994 8 16 16
2800 Tokyu Car Japan 2000 10 20 20
2700 Alstom Spain 2000 14 28 Stored
152
EMU 8100 LHB Siemens Germany 1984 38 76 76
8500 Tokyu Car Japan 2000-2005 17 68 68
8200 Alstom Spain 2000 5 10 Stored
144
Intercity MkIV CAF Spain 2006 8 67 (3 spare coaches) 67
22000 Hyundai Rotem Korea 2007-2012 63 234 (3/4/5 car sets) 234
Enterprise De Dietrich France 1996 4 28 (14 owned by NIR) 14
315
Loco 201 GM-EMD Canada / USA 1995 22 (+12 stored) 22
71 GM-EMD USA 1976 18 18
40
651
Passenger Numbers – Here we go again!!
Fleet requirements
• Passenger growth 8% - 10% per year
• Peak availability really pushed
• DARTS 6/8 car all day, 10 min interval – 85% availability
• ICR fleet – 85%+ availability
• Commuter fleet – 90% availability
• All other fleets 75% - 80% availability
Time for some technology optimised maintenance
• Improving the maintenance response through understanding system condition in real time • Collect data, analyse it and report by exception
Traditional Maintenance Concept
General
Maintenance
and Overhaul
Manual
What's wrong with the OEM manual? • Not optimised for the maintainer – for cost, reliability or availability • Very generic content – not region specific • Intervals are not based on experience or actual usage e.g Km
Objectives for Application of Technology
Safety
Availability Cost
ASSET
Reliability EfficiencyMaintenance Types
•Reactive
Where we are
•Proactive
Where we want to be
•PredictiveIrish Rail 2018 – current systems in use
RailBam
Nexala
W.I.L.DBasic data system concept
Mobility solutions • Mobility solution deployment 2018 / 2019 • Tablet solution connectivity • On site exam entry • Access to technical info • key data access (SAP, Nexala, WILD etc)
Remote Diagnostics • IE Network mainly single track and remote • Train failure recovery and prevention is key! • Complex new Hyundai Rotem fleet • 234 vehicles in 63 trains • Major reliability challenge • TMS on board with 24 hour memory!! • Solution – remote diagnostics system
Remote diagnostics deployment
• Competitive tender 2012
• Nexala chosen
• Hyundai Rotem fleet fitted 2012
• €4m+ total costs (Capex and Opex) to 2018
• Retrofitted to 70% of fleets by end 2018:
• CAF DMUs Class 29000 - complete
• CAF MKIV Intercity carriages – in progress
• GM Class 201 Locomotive – in progressNEXALA Diagnostics
Wheel slip hotspot activity by location during autumn
Who are the system users? • Fleet technical support – shift / on call • Depot & fleet management • Fleet planners • Powertrain maintenance supplier • Nexala “Superuser” System development team • CTC (location, fleet status) • Safety • Operations • Infrastructure
Typical usage? • Live fault and defect analysis – 800 data channels per vehicle • Tracking Km run and Engine hours • Root cause analysis • Event recorder downloads • Fleet location
Nexala rules
Building rules, alerts and managing output
• From the root cause analysis investigations, rules can be built to monitor
specific and multiple channels to alert users
• For potential failures an email alert can be generated to alert of a sequence
of events occurring on a train that is a “known” critical path to a failure.
This is the key reliability growth tool and allows Predictive MaintenanceCase study: Battery charger live fault • This is a critical fault event
Case study: Battery charger live fault • Rule generated email / SMS with actions required
Case study: Battery charger live fault • Resent after 30 mins if fault still present
Case study: Transmission Temperature Alarm Based on data - Maintenance decision to disconnect hydro brake to allow set remain in service. (324 transmission temp). Procedure developed for disconnecting the hydro brake to allow set to run in service until set returns to maintenance Depot
Case study: Service affecting air pressure issue Driver advised a full brake application received on approach to Heuston NFF. Nexala data was reviewed & fault occurred so a repair was required.
Case study: HVAC passenger complaint “Dear Sir/Madam I recently was in contact with your call centre and was told to contact you by email to make a complaint. On Saturday 10th of June I had the misfortune of travelling on the 13.25 service from Heuston to Galway. I was seated in the first carriage. The temperature inside the train was very high. Myself and my fellow passengers expected the train to cool down once it started moving. This was not the case however. It was so hot that I thought I was going to faint, there were many elderly passengers who were in a lot of discomfort. As there was no way of opening a window I decided to look for someone to Help. Not only was there no staff on board but there was no one selling any drinks which would have been of some help (all the staff were on the next train which was joint on to our train and there was no way to go between the trains). As the journey continued the heat became more and more unbearable. This was now a serious health and safety concern. The man sat opposite me very kindly started sharing his water with myself and the people around us. Eventually by the time we reached Athlone there was an announcement telling people to get off the train and to go into the train in front. This resolved the problem. I could not believe this was allowed to happen. I explained that many people on the train were near fainting and that you couldn't even get a drink of water to a member of staff in Athone. However he just looked at me saying they were working on it. At the very very least I expect my fare to be refunded and some compensation to be paid for the torture I had to put up with by being an Irish Rail customer. I am prepared to send a copy of this correspondence to the media if my complaint is not dealt with satisfactory.”
Case study: HVAC passenger complaint
• Sets 22023 & 22003 were coupled. 22203 was the leading car. This passenger was on set 23. There was
an engine isolated on car 22423, this means 2 x HVAC’s on reduced output so it will take longer to reach
the required temp.
• The temp was up at the start of the journey because the HVAC only started to work at 13:15hrs (10 mins
before departure). Temp reached normal.Case study: Passenger complaint doors not operating
• 19.35hrs Dublin to Galway on Friday 10/2/17. Passenger wanted to get off at Woodlawn (21.26hrs) Carriage C
door did not operate. Passenger was over carried. Passenger got the 22.15hrs service from Galway back to
Woodlawn. Passenger has requested an immediate reply
• Set 48 was on the service. All doors were operating fine on this service. Set stopped in Woodlawn between
21:45:30hrs & 21:46:15hrs (see below). Doors were enabled for this time giving a green light to all doors on the
platform side only.Case study: Passenger complaint doors not operating
• When the set stopped in Woodlawn door B was opened (see below in yellow). Circled below in red indicates
someone trying to open a non platform side door.Predicative Maintenance Use • Rules / algorithms can be run regularly. About 150 “rules” running for Rotem fleet currently • Use output to predict when maintenance intervention needed. E.g • Air leaks – Monitor compressor run time. Outside norm = leak • Doors – Monitor opening / closing times. Outside norm = likely failure • Battery charger – Monitor key events and alarms • Transmission temp – regularly above 125degC = likely failure
Results – NEXALA R2M • Balanced maintenance 2 weekly exam now 5 weekly (Km) • System supports distance based maintenance regime • Service affecting failures down > 50% • Huge increase in engine and train reliability • Tech issues addressed without disruption • Customer issues diagnosed and addressed • Added benefits
Results – NEXALA R2M
Engine Reliability Improvement
NEXALA
FITMENTBehind the scenes……….. • Icomera Wi-Fi gateway “hosts” our Nexala Remote diagnostics system on ICR fleet • Remote uploading of timetable databases, PIS databases • Seat reservation system manifest connectivity
Passenger Wi-Fi
Solution Overview- Train components
• Icomera X6 / X4 main gateway
• Acksys or HP access points
• Huber & Suhner roof antennae
Antennas+ GPS
Access Point Access Point
Gateway
Router
Network NetworkRailBAM Bearing Acoustic Monitor • Expected bearing life approx 3m Km (= Major cost €€) • Premature bearing failures at
RailBAM Acoustic Monitor • 3 x Lineside acoustic systems installed 2015 - cost €3m • Acoustic monitoring of axle bearings
RailBAM Site Overview
RailBAM System Video
41System principles
Bearing develops a
defect
Vibration in the bearing structure
generates acoustic noise
Noise Frequency Noise amplitude
determines the determines
fault types damage severity
Alert
Maintenance decision.Fault Categorisation
FAULT
SEVERITY
Roller Severe
Cup Moderate
Cone Minor
Multiple
ExtendedTypes of fault detected Cup/Cone Roller Multiple Extended
Acoustic signature of failing bearing
Site Recording and valueInspections of 2 x bearings following alerts
22331 10 Consistent RS1_p alerts 22222 10 Consistent RS1_p & e alerts
Inner race fault Inner race FaultRailBAM Web Portal – vehicle mimic
• System reports alarms
• Data can be interrogated
• Web mimic highlights
areas for investigation
• Example shows RS3
faults
David Pender
CME Projects 47
Dept.Results – RailBAM • Successful early detection of axle bearing failures • 14 alarms on ICR fleet = 10 damaged bearings (since Nov 2015) • Greater confidence for fleet operation • Consider using system output to drive future overhaul plan • Note - Hot box detector system still has a role in network safety
Wheel Impact Load Detector - W.I.LD
• Co-located with Railbam
• Data reported in same system
• System of load cells measures
rolling wheel force in kN
• Also measures % axle imbalance
• Captures wheel flats early –
reduces damage to wheel & rail
Wheel Impact Load Detectors
49W.I.L.D alarm levels – per fleet type
W.I.LD Wheel damage example Wheel damage measured over the site by high readings - Actual damage to the wheel Trend analysis Impact vs time Shows the condition of the wheel flat progressing
Wheel damage
Wheel measurement
Current wheel measurement tools
AAR gauge
Profile gauge
ORE gaugeWheel measurement
• Manual inspect and measurement – 3 / 6 month typically (Tolerance +/- 3mm)
• South West Trains UK – 3 / 4 mm variance between staff measuring the same wheel!!
• Human Error potential well documented in engineering industry
• IE fleet wheel life ranges from 18 months – 5 years (scope for savings)
• Track based systems can inspect and measure several times a day to 0.5mm
toleranceAutomatic wheel inspection
Vehicle inspection systems
Aurizon – Queensland Coal Freight Aurizon – Coal train operator • 6300 wagons, 300 Locomotives • Trains 2.4km – Cargo Aus $2m • “Parting” a major issue • Hopper doors a major issue • Wheelset & bearing life high cost 6 x Supersites
SUPERSITE - Inspect, measure, assess and report:
• Wheel dimensions
• Wheel condition
• Back to Back
• Axle bearings
• Bogie equipment
• Brake pad/shoe size
• Brake shoe carrier
• Pantograph
• Axle load imbalance
• Missing fasteners
• Missing equipment
• Brake disc size and condition
• Coupler / headstock alignment
Machine Vision Algorithms
• Hopper doors & equipmentAURIZON SUPERSITE – MVA images
AURIZON SUPERSITE - Headstock / coupler deformation
AURIZON SUPERSITE – Wagon hopper door mechanisms
AURIZON SUPERSITE - Missing critical fasteners underframe
AURIZON SUPERSITE - RESULTS • Each train pass generates data file of 5Tb • Data reviewed locally – exception report • 16 major detections per month • Critical safety events - 87% • Brake blocks – 40% • Wheel consumption – 47% • Inspection interval doubled to 84 days • HM interval doubled to 24 months • Unscheduled maintenance - 50% • Shunting – 50% • Availability - +3% ( = 1 x train)
VR Finland (Helsinki) • Entire fleet covered • 7 years in service • Works well in the snow!
SNCF Paris • Depot site inspects 60 x TGV sets per day
Consider the future – Diesel is not Dead!
• Rail development strategies published
• Move towards sustainable mobility –
progressive move to electrification for
2040
• IE company strategy adopted in 2016 -
no more diesel only trains
Future trains will be:
• Electric only or
• Diesel Bi mode or
• Diesel Battery hybridConsider the future
• Electrification to Maynooth, Drogheda and
Hazelhatch
• Bi mode trains allow DART expansion earlier
than Electrification
• 10 yr framework order for 300-400 vehicles
Removable mini
generator carBattery Hybrids • Diesel Battery “Prius” gives +30% energy saving • Battery Electric allows 50km running “off wire”
Conclusion and Summary • Technology now available to optimise and transform maintenance • Key objectives for Technology met • Safer through more frequent measured inspections • Reduced costs through optimised output • New fleets will be specified with “Supersite” & Diagnostics • Need to channel the “Big Data” to a central point of use
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