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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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