Reliability from DATA A framework for technology OMDEC 1. 2. 3. 4. 5. 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? 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 Work orders Condition monitoring data relevant to the failure modes of interest. RCM knowledge of failure modes. RCM Achieving reliability from data Four challenges must be overcome: 1. 2. 3. 4. Data extraction and transformation Management of the work order – RCM relationship Sample generation Typical focus Reliability analysis Unified EXAKT Process •Systematic •Quick •Results oriented Challenge 1 Data extraction, transformation Input from CMMS Data transformations Example: FMEA extraction Output for LRCM Input from RCM Cost, RCMO, RCM Toolkit, etc Example: Work order extraction Input from Ellipse CMMS input Data transformations Challenge 2 LRCM … the most difficult of the four - the key challenge Text of the selected knowledge record KPIs Event type indicators: PF (blue), FF (red), S (yellow). Add/Edit KRs (with audit trail) “Slice and dice” Text of the selected work order Dynamically, 1. Link the work orders and knowledge base. in the day-to-day work order 2. Build the knowledge base… process Challenge 3: Sample generation RCM Knoweldge base Work Orders that have been linked to the KB Events table (the sample) Sample generation /Challenge 3 cont’d: CMMS Work orders Events table EF15 Work ord. 1, FF RCMREF15 EF16 Work ord. 2, FF RCMREF16 B16 EF16 Work ord. 3, FF RCMREF16 B16 ES15 Work ord. 4, S RCMREF15 B15 EF15 Work ord. 5, PF RCMREF15 B15 Legend: Life cycles: Left Suspensions: Right (Temporary) Suspensions: EF: endings by failure ES: endings by suspension Sample Calendar Time B15 Hazard model 0.781 t h(t ) 2709 2709 0.7811 e0.06944MaxWSDrop Challenge 4: Reliability analysis and EXAKT + Predictive Model Predictive model RULE and Confidence interval + Cost model Decision based on: Probability EXAKT Decision based on: Scatter Cost and Probability RULE Challenge 4 - Achieving Reliability from data in EXAKT Age data (CMMS) CBM data Cost data Hazard model Transition model Cost, Availability, Profitability model RULE Maintenance Decision Supplied by user Modeling Software Intermediate results Final Result Challenge 4 - CBM+Simulation in SPAR-PHM No maintenance Replace radio now And plan overhaul in 6 months Projected worst actor following overhaul OMDEC methodology “living reliability” “on-the-job” Iterative Integrated LRCM Pilot On-the-job process Overcoming Key Challenge 2 Team 1. 2. 3. 4. 5. 6. Monitor work orders & KR links Monitor knowledge record updates Ask questions Propose changes Get feedback Get consensus. On the job teamwork Progress reports KPIs LRCM OMDEC guidance LRCM specialists + Company’s Work orders Methods, and KR links Engineers, planners, supervisors, technicians analyses Company’s Maintenance Management Knowledge records models Leadership: 1. Recognition, 2. Empowerment, 3. Interest OMDEC team participants Murray Wiseman – LRCM, CBM specialist Dr. Daming Lin – Maintenance data statistician and reliability expert, signal processing, reliability software, database + ETL specialist.