Transcript NFF – A Practical Perspective for Modern Rolling Stock

Reducing the Impact of NFF through
System Design Symposium – EPSRC
TES Centre
Dr Joanne Lewis CEng FIMechE
18th March 2013
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NFF – A Practical
Perspective for
Modern Rolling Stock;
Dealing with NFF in
Reliability Growth
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INTRODUCTION OF NEW ROLLING STOCK – THE PROBLEM
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WHAT DID WE DO?
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HOW DID WE DO IT?
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WHAT’S NEXT – DIAGNOSTICS & PROGNOSTICS
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WHAT HAVE WE LEARNED?
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FEEDBACK
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1
Bombardier’s New Underground Rolling Stock
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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191 trains
• 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
Flatlining
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Continuous
Growth
Stabilisation
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What is the problem?
What did we do?
Design Principles (How)
 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)
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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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Governance (Who & What)
How did we do it?
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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.
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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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Aggressive FRACAS applied via Train Reliability Improvement Program
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)
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6%
8%
19%
4%
8%
5%
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NFF
How did we do it?
Look for patterns in the symptoms/data - look for clues to support the observations made by
the Driver
List 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 etc
Non-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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Interview staff, review Driver’s reports
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 morning
Observed 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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Repeated the brake system test a lot and found that the test eventually failed
Bombardier Asset Condition Data to Information Flow
Bombardier AIMS Control Centre
Assets
Mitrac/TMS/TCMS
and/or OTMR
Remote Data
Transfer
Knowledge Control Centre (KCC)
 Data Filtering, Interrogation,
Visualisation & Analysis
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Faults & Events
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Time Series
Information
& Advice
etc
Data
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Information
Automated Alerts
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Customer
• Maintenance
• Operations
• Planning
Operations Control Centre (OCC)
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Information
& Advice
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Red, Amber, Green status
- Engines
- HVAC
- Doors
etc
Engineering Review
Suppliers
& Partners
• Data & Manuals
• Parts Supply
etc
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Bombardier Orbita
Anticipation - What’s Next?
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
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†
Prognostic – ability to predict SAF before they are realised including system degradation
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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
Review of Fault Analyses
 FMECAs declare
diagnostics for
only 22% of failure
modes
 Potential
diagnostics could
be increased to
53% of failure
modes
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Each FMECA has been critically reviewed jointly by the System Engineer and
RAMS Engineer to identify actual and potential diagnostic and prognostic
capabilities
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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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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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What Have We Learned?
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Feedback
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? Q&A
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