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Reliability from DATA

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

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  1. Reliability from DATA A framework for technology OMDEC

  2. 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

  3. 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.

  4. 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?”

  5. 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.

  6. 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

  7. 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

  8. 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…

  9. Challenge 3: Sample generation RCM Knoweldge base Work Orders that have been linked to the KB Events table (the sample)

  10. 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

  11. 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

  12. 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

  13. Challenge 4 - CBM+Simulation in SPAR-PHM No maintenance Replace radio now Projected worst actor following overhaul And plan overhaul in 6 months

  14. OMDEC methodology “living reliability” “on-the-job” Iterative Integrated

  15. 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.

  16. 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

  17. OMDEC team participants • Murray Wiseman – LRCM, CBM specialist • Dr. Daming Lin – Maintenance data statistician and reliability expert, signal processing, reliability software, database + ETL specialist.

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