1. Addressing Challenges and Opportunities in Evidence Based Asset Management Andrew K.S. Jardine, Centre for Maintenance Optimization & Reliability Engineering (C-MORE) Department of Mechanical & Industrial Engineering University of Toronto, Canada

9. EXAKT Optimal Decision: A New “Control Chart”

11. Summary: Principle of CBM Optimization Age Data Condition- Monitoring Data Maintenance Decision Hazard Model Cost and Availability Model Remaining Useful Life Engineering Judgment Supplied by user Software engine Intermediate result Final result

12. Limited Hard Data: Eliciting Tacit Knowledge COMADEM 2009 San Sebastian, Spain

13. Hazard Model

14. Combining Tacit Knowledge With Hard Data Experts’ knowledge Prior distribution Posterior distribution

15. Sample Question of Interviews with Experts Main A Main B Length 1 KM 1KM Number of leaks over the last 5 years 1 leak(s) 1 leak(s) Level of stray current A= high: P/S reading fluctuation > 199 mv, B= medium: P/S reading fluctuation 100 mv-199 mv, C= low: P/S reading fluctuation < 100 mv) A A Soil type (sand, clay, pea gravel, loam, wet clay, shale) sand clay Type of coating (coal tar, yellow jacket, fusion bond epoxy) Yellow Jacket Yellow Jacket Corrosion history (percent of the time the cathodic protection system has been up and working over the last 5 years) 100 percent 70 percent Age 40 years 40 years How much the probability of encountering a leak during the next year in Main B is higher than in Main A ? Please provide an interval for your estimate that makes you confident that the answer lies within that interval. Lower bound= Upper bound=

16. Model Parameters Parameters Expert Opinion Only Data + Expert Opinion Shape ( β ) 2.25 3.69 Scale ( η ) 104.5 101.58 Coating 0.53 0.38 Soil Type 2.27 2.32 f (length, no. of services) 1 1 Stray current 0.33 0.35 Corrosion Protection Uptime -0.64 -0.15 f (leak history) 1 1

17. Hazard Model (with values)

18. Addressing Challenges and Opportunities in Evidence Based Asset Management New Tool: Knowledge Elicitation