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Reliability from DATA. A framework for technology OMDEC. Maintenance / Asset Management Consulting Training Programs Software Tools “Living RCM” Canadian Company: Ottawa, Montreal, Toronto, and Australia Locations

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Reliability from data

Reliability from DATA

A framework for technology

OMDEC


Reliability from data

  • Maintenance / Asset Management Consulting

  • Training Programs

  • Software Tools

  • “Living RCM”

  • Canadian Company: Ottawa, Montreal, Toronto, and Australia Locations

    Sample Industries: Mining, Oil & Gas, Utilities, Fleets, Government and Military


Why collect data
Why collect data?

  • Only one reason: To perform analysis. - “Reliability Analysis”

  • Why analyze?

    • To improve the process of maintenance continuously. (CPI = Continuous Process Improvement)

  • Why CPI?

    • That’s our (i.e. everyone’s, particularly management’s) job.

  • Why?

    • Economic survival of the fittest. Keep up with change.


The false promise of cbm technology
The “false” promise of CBM technology

  • Based on the logic that:

    • The more data the better,

    • The faster the better, and

    • The more views (PDAs, iPhone, etc) the better.

    • All of the above are good, but there is a flaw in the logic.

  • What is the logical flaw?

    • There is an infinite supply of the wrong data.

    • The logic skirts the question: “What is the right data?”


What s the right data

RCM

Work orders

What’s the right data?

  • Age (“life”, “life cycle”, “event”) data

    • Failure Mode occurrences with attributes:

      • event type (PF, FF, S, …),

      • RCM reference,

      • working age

  • Condition monitoring data

    • relevant to the failure modes of interest.

  • RCM knowledge of failure modes.


  • Achieving reliability from data

    Typical focus

    • Unified EXAKT Process

      • Systematic

      • Quick

      • Results oriented

    Achieving reliability from data

    Four challenges must be overcome:

    • Data extraction and transformation

    • Management of the work order – RCM relationship

    • Sample generation

    • Reliability analysis


    Challenge 1 data extraction transformation

    Input from CMMS

    Data transformations

    Output for LRCM

    Output for LRCM

    Data transformations

    Input from CMMS

    Ellipse input

    Challenge 1 Data extraction, transformation

    Example: FMEA extraction

    Input from RCM Cost, RCMO, RCM Toolkit, etc

    Example: Work order

    extraction


    Challenge 2 lrcm the most difficult of the four the key challenge

    Text of the selected knowledge record

    Event type indicators: PF (blue), FF (red), S (yellow).

    KPIs

    “Slice and dice”

    Text of the selected work order

    Dynamically,

    in the day-to-day work order process

    Challenge 2 LRCM …the most difficult of the four - the key challenge

    Add/Edit KRs (with audit trail)

    • Link the work orders and knowledge base.

    • Build the knowledge base…


    Challenge 3 sample generation
    Challenge 3: Sample generation

    RCM Knoweldge base

    Work Orders that have been linked to the KB

    Events table (the sample)


    Sample generation

    EF15

    Work ord. 1, FF RCMREF15

    B15

    EF16

    Work ord. 2, FF RCMREF16

    B16

    EF16

    Work ord. 3, FF RCMREF16

    B16

    Sample

    ES15

    Work ord. 4, S RCMREF15

    B15

    EF15

    Work ord. 5, PF RCMREF15

    B15

    Legend:

    Left Suspensions:

    Life cycles:

    Right (Temporary) Suspensions:

    EF: endings by failure

    ES: endings by suspension

    Sample generation

    CMMS Work orders

    Events table

    /Challenge 3 cont’d:

    Calendar Time


    Challenge 4 reliability analysis and exakt

    Hazard model

    +

    Predictive model

    Cost model

    +

    Decision based on:

    Probability

    EXAKT Decision based on:

    Scatter

    Cost and Probability

    RULE

    Challenge 4: Reliability analysis and EXAKT

    Predictive Model

    RULE and

    Confidence interval


    Challenge 4 achieving reliability from data in exakt
    Challenge 4 - Achieving Reliability from data in EXAKT

    Hazard model

    Transition model

    Cost, Availability,

    Profitability model

    Supplied by user

    Modeling Software

    RULE

    Maintenance

    Decision

    Intermediate results

    Final Result

    Age data (CMMS)

    CBM data

    Cost data


    Challenge 4 cbm simulation in spar phm
    Challenge 4 - CBM+Simulation in SPAR-PHM

    No maintenance

    Replace radio now

    Projected worst actor following overhaul

    And plan overhaul in 6 months


    Omdec methodology living reliability

    OMDEC methodology “living reliability”

    “on-the-job”

    Iterative

    Integrated


    Lrcm pilot on the job process overcoming key challenge 2
    LRCM Pilot On-the-job processOvercoming Key Challenge 2

    Team

    • Monitor work orders & KR links

    • Monitor knowledge record updates

    • Ask questions

    • Propose changes

    • Get feedback

    • Get consensus.


    On the job teamwork

    OMDEC

    LRCM specialists

    +

    Company’s

    Engineers, planners,

    supervisors, technicians

    Company’s

    Maintenance

    Management

    Progress reports

    KPIs

    LRCM guidance

    Knowledge records

    Work orders

    and KR links

    • Leadership:

    • Recognition,

    • Empowerment,

    • Interest

    Methods,

    analyses

    models

    On the job teamwork


    Omdec team participants
    OMDEC team participants

    • Murray Wiseman – LRCM, CBM specialist

    • Dr. Daming Lin – Maintenance data statistician and reliability expert, signal processing, reliability software, database + ETL specialist.