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NFF – A Practical Perspective for Modern Rolling Stock; Dealing with NFF in Reliability Growth
Reducing the Impact of NFF through System Design Symposium – EPSRC TES Centre
Dr Joanne Lewis CEng FIMechE
18th March 2013
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INTRODUCTION OF NEW ROLLING STOCK – THE PROBLEM
WHAT DID WE DO?
HOW DID WE DO IT?
WHAT’S NEXT – DIAGNOSTICS & PROGNOSTICS
WHAT HAVE WE LEARNED?
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FEEDBACK6
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Bombardier’s New Underground Rolling Stock
191 trains
London Underground Metropolitan Line, District Line, Hammersmith and City Line
New Technology & Novel Design
Complex
Aggressive operating environment
London 2012 Olympic Games
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What is the problem?
FlatliningContinuous
Growth Stabilisation
• Trains not highly reliable from first introduction into passenger service
• Contracts provide for this by defining reliability growth curves which describe where we need to be in order to achieve our contractual targets
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What did we do?
Design Principles (How)
Governance (Who & What) Train Reliability Improvement Program (TRIP) – Dedicated multi-functional team Engineers, Project Management,
Analysts, Procurement, Suppliers – Organisation & Objectives
Zero Tolerance of SAFs & SAF Management
FRACAS & Process Improvements (How) Bombardier FRACAS Process Reactive too slow Aggressive FRACAS via TRIP Modifications – process improvements, impact prediction & monitoring Measure and Monitor (Duane modelling, PDCA, ‘Failure to Fix’ etc.) Root Cause Analysis – correct and fast diagnosis of failures (fix the ’knowns’)
Anticipation – Failure Prediction; what’s next? FMECAs Use of diagnostics/prognostics/condition monitoring prevention of failures not rectification following failure. Reveal
the ‘unknowns’ which effect reliability and rectify them ASAP.
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How did we do it?
Aggressive FRACAS applied via Train Reliability Improvement Program Management Team set up solely dedicated to SSL reliability growth - ensures a strategy to target areas for remedial action Goals set for ‘failure to fix’ Daily support to depot Dedicated team of modification writers to be used to ensure both speed and quality of modification proposals is optimised Every failure in the field followed up – zero tolerance on SAFs Excepting safety issues, modifications and actions to deal with SAFs are prioritised Bespoke software tools to support the process and dedicated team to record and manage events Monitoring, analysis and reporting functionality Real-time system used across all projects A single source of data Weekly FRACAS meetings - interdisciplinary teams Improvements actioned with fast turn around times
Improved Root Cause Analysis Every new failure mode investigated using formal RCA technique
– Larger complex problems (e.g. s/w and h/w, key reliability-critical systems with many stakeholders) Kepner Tregoe– Other problems 8D/A3, 5 Whys, Ishikawa, 6 Sigma, HAZOP etc.
RCA to be completed within 7 days of new failure mode being identified (to support engineering solution) Actions defined to confirm/eliminate potential root cause(s) Modification written using RCA as evidence RCA to be provided by supplier OR led by Bombardier with supplier actively involved Use of Orbita ‘real time’ analysis
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How did we do it?
Warranty Process Challenge NFF reports with more depot
detail relating to the failure Get the supplier to the depot to experience
the fault first hand Visit the supplier to check how they test
the vehicle - is it representative? Quarantine the part if the depot has doubts
and try on another vehicle/unit before returning to supplier
Introduce additional testing of the part before sending to the supplier e.g. test rig at depot
Agree a ‘joint investigation’ after 3 NFF failures (contractual term)
6% 4% 8% 5%
NFF
8% 19%
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How did we do it?
Look for patterns in the symptoms/data - look for clues to support the observations made by the DriverInterview staff, review Driver’s reportsList all possible causes using Fault Trees/FMECAs (A3 sheets)Use the failure evidence and engineering judgement to rank the causes
Not enough info? gather more data
Trends? Same location? Same time of day? At the same point in a sequence of functions?Test a ‘normal’ system/equipment stress the system until it fails – lots of cycles, higher operating temperatures etcNon-intrusive diagnostic tools to confirm functionality is correct and that the signal responses are correct (Orbita, Train Data Recorders or DCUTerm)
DCUTerm allows us to sample signals at intervals from 16ms to 1sec Realtime analysis of signals allows checking of issues created due to the timing of signals
Check the findings against the possible causes list and either eliminate potential causes or confirm causes – check next cause
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Example – Pressure Switch in the Braking System
Observed sporadic failures of the daily brake test to check timing of the brake release function. Test passed if brakes released within 3sec.Failures usually occurred early morningRepeated the brake system test a lot and found that the test eventually failedObserved that the brake release times varied and crept towards the 3sec threshold
Test done in warm weather 2.9sec Test done in cold weather 3.1sec
Software modification was deployed to permit the brake release function to happen after 5sec. In parallel the pressure switch design being revisited with the supplier to improve the ability to meet the original requirement
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Bombardier Asset Condition Data to Information Flow
Mitrac/TMS/TCMS and/or OTMR
Assets
Customer• Maintenance• Operations• Planning Information
& AdviceOperations Control Centre (OCC)
Remote Data Transfer
Bombardier Orbita
Data Filtering, Interrogation, Visualisation & Analysis
• Faults & Events• Time Series
etc
Data Information Automated Alerts
• Red, Amber, Green status- Engines- HVAC- Doors
etc Engineering Review
Bombardier AIMS Control Centre
Knowledge Control Centre (KCC)
Suppliers& Partners
• Data & Manuals• Parts Supply etc
Information& Advice
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Anticipation - What’s Next?
Reliability growth has been centred upon: a reactive approach to addressing reliability affecting faults as they arise; identification of the immediate/root cause of the deviation from the design intent; and, rectifying these deviations ASAP
Bombardier recognised that to make significant performance improvements a proactive SAF prevention program was key
Use of Diagnostic Data Use has been made of diagnostic data gathered by the trains with daily reviews of the data by Engineering
Systematic Review of Fault Analysis to Improve Diagnostic/Prognostic† Capability Trains subject to extensive FMECA fault analyses addressing each Bombardier and supplier component (including
specific analyses of immobilisation events) Vehicle diagnostic capabilities of the supplier’s design have been captured within the FMECAs to varying degrees
with emphasis upon maintenance inspections to diagnose component faults This analysis has been used to assist in the prediction, diagnosis and aversion of faults arising on the train thus
minimising exposure to SAFs
† Prognostic – ability to predict SAF before they are realised including system degradation
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Review of Fault Analyses
Each FMECA has been critically reviewed jointly by the System Engineer and RAMS Engineer to identify actual and potential diagnostic and prognostic capabilities
FMECAs declare diagnostics for only 22% of failure modes
Potential diagnostics could be increased to 53% of failure modes
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Potential Prognostic Capabilities Reflected in FMECAs
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Potential Prognostic Capabilities Reflected in FMECAs
Less than 20% of failure modes in FMECAs have potential for prognostics
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What Have We Learned?
• Governance well established• Processes for SAF management deployed• Improved diagnostics deployed• Reveal the unknowns• Focus SAF prevention and NFF reduces
Reduction in flatlining
5x improvement
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Feedback
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Q&A
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