Reliability from DATA A framework for technology OMDEC

1. Maintenance / Asset Management Consulting 2. Training Programs 3. Software Tools 4. “Living RCM” 5. Canadian Company: Ottawa, Montreal, Toronto, and Australia Locations Sample Industries: Mining, Oil & Gas, Utilities, Fleets, Government and Military

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 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? 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. Work orders RCM

Achieving reliability from data 1. Data extraction and transformation 2. Management of the work order – RCM relationship 3. Sample generation 4. Reliability analysis Four challenges must be overcome: Unified EXAKT Process Systematic Quick Results oriented Typical focus

Challenge 1 Data extraction, transformation Example: FMEA extraction Example: Work order extraction Ellipse input Input from CMMS Input from RCM Cost, RCMO, RCM Toolkit, etc Output for LRCM Data transformations

Challenge 2 LRCM … the most difficult of the four - the key challenge Text of the selected work order Text of the selected knowledge record Event type indicators: PF (blue), FF (red), S (yellow). Add/Edit KRs (with audit trail) “Slice and dice” KPIs 1.Link the work orders and knowledge base. 2.Build the knowledge base… Dynamically, in the day-to-day work order process

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

Sample generation Work ord. 1, FF RCMREF15 Work ord. 2, FF RCMREF16 Work ord. 3, FF RCMREF16 Work ord. 4, S RCMREF15 Work ord. 5, PF RCMREF15 CMMS Work ordersEvents table EF15 B15 EF16 B16 EF16 B16 ES15 B15 EF15 B15 Sample Right (Temporary) Suspensions: Legend: EF: endings by failure ES: endings by suspension Life cycles: Left Suspensions: Calendar Time /Challenge 3 cont’d:

Challenge 4: Reliability analysis and EXAKT Hazard model + RULE and Confidence interval Cost model EXAKT Decision based on: Cost and Probability Decision based on: Probability RULE Scatter + Predictive model Predictive Model

Challenge 4 - Achieving Reliability from data in EXAKT Age data (CMMS) CBM data Cost data Supplied by user Modeling Software Intermediate results Final Result Cost, Availability, Profitability model Hazard model Transition model RULE Maintenance Decision

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

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

LRCM Pilot On-the-job process Overcoming Key Challenge 2 1. Monitor work orders & KR links 2. Monitor knowledge record updates 3. Ask questions 4. Propose changes 5. Get feedback 6. Get consensus. Team

OMDEC LRCM specialists + Company’s Engineers, planners, supervisors, technicians LRCM guidance Methods, analyses models Knowledge records Work orders and KR links On the job teamwork Leadership: 1.Recognition, 2.Empowerment, 3.Interest Company’s Maintenance Management Progress reports KPIs

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