An uncertainty management approach to a maintenance decision for an ageing system Thor Erik Nøkland Roger Flage Terje Aven

2 0 Time Proneness to failure Increasing failure occurrences Ageing systems Duration of downtime Time 0 Unavailable spare parts Redesign/modification Major challenges:

3 Decision problem A.Repair when failure (Corrective maintenance) B. Replace/redesign now (Preventive maintenance) What are the consequences? System performance Costs

4 Traditional analysis Results from traditional analysis provide useful insights and decision support, however… 1. Develop a stochastic model: Y(t) = f(X(t)) 2. Determine cost parameters: -Repair -Replacement cost -Lost production/waiting 3. Specify optimisation criterion: -Total expected discounted cost 4. Find optimal maintenance policy

5 Uncertainty Lack of data Little knowledge about ageing mechanisms Unavailability (future) of spare parts The results are conditioned on the assumptions in the analysis

6 An uncertainty & management based approach Focus: handling and communicate uncertainty Uncertainties related to ageing phenomena and process An evaluation of the assumptions made in the availability and cost analysis Manageability factors

7 Risk/uncertainty assessment framework Ageing system Signs of ageing Historical data Traditional analysis Availability model Cost model Model parameters Optimisation criterion Optimal maintenance policy/decision Sensitivity analysis Importance ranking Uncertainty and manageability assessment Uncertainty factors  Sensitivity  Degree of uncertainty  Manageability  Uncertainty reduction measures Decision alternatives Presentation and evaluation of results Managerial review and judgement Decision

8 Examples - Results Uncertainty factor Treatment in availability analysisDegree of sensitivity Degree of uncertainty Degree of manageabilit y Uncertainty reducing Measures Comments HMLHMLHML 1. Number of ageing components No ageing trends found in available data. Ageing components selected in FMECA session. xxx- studies - monitoring 2.Degree of ageing Weibull life distribution with parameters that fit historical MTTF. Shape parameter 1,5. xxx- inspection - tests - studies Important for analysis result 3.Degree of repair performed on ageing components Components w/ several failure modes: “as bad as old” – i.e. set in the condition they were right before failure. Components w/one failure mode: “as good as new” xxx- data collection 4. Difference between duration of replacement and regular repair Duration of replacement and regular repair is the same when spare parts are available. “Spare part order time” is added when not available. xxx

9 Presentation and evaluation of results Uncertainty factor Degree of sensitivity Degree of uncertainty Degree of manageability Comments HMLHMLHML Number of ageing components xxx Degree of ageing xxxImportant for analysis result Degree of repair performed on ageing components xxx Difference between duration of replacement and regular repair xxx A broader risk/uncertainty picture Figure 1Figure 2

10 Sensitivity 2012: P % Expected value - 72% P % P % Expected value - 65% P % α = 1.5α = 1.7 Changing the assumption that uncertainty factor 2 leads to a complete new set of results

11 Conclusions Otherwise uncertainties could be camouflaged Facilitate communication about uncertainty The extra resources needed for producing this extended risk and uncertainty picture is justified

12 Thank you for your attention!

13 List uncertainty factors 1.Number of ageing components 2.Degree of ageing in components believed to be ageing 3.Availability of spare parts in the future 4.Duration of waiting times associated with redesign and modifications to the system to fit non-original components 5.Degree of repair performed on ageing components 6.Difference between duration of replacement and regular repair 7.Number of redesigns for a component 8.Duration of annual planned/scheduled maintenance 9.Failure types included in reliability data 1. Identify uncertainty factors a) Related to ageing phenomena/processes b) Related to assumptions in the traditional analyses 2. Evaluate treatment in traditional analysis 3. Assess and classify degree of sensitivity 4. Assess and classify degree of uncertainty 5. Assess and classify manageability 6. Suggest uncertainty reducing measures

14 Sensitivity asessment Significant sensitivity: Relatively small changes in base case values result in altered conclusions Moderate sensitivity: Relatively large changes in base case values needed to bring about altered conclusions Minor sensitivity: Unrealistically large changes in base case values needed to bring about altered conclusions 1. Identify uncertainty factors a) Related to ageing phenomena/processes b) Related to assumptions in the traditional analyses 2. Evaluate treatment in traditional analysis 3. Assess and classify degree of sensitivity 4. Assess and classify degree of uncertainty 5. Assess and classify manageability 6. Suggest uncertainty reducing measures

15 Uncertainty asessment Significant uncertainty: One or more of the following conditions are met: –The phenomena involved are not well understood; models are non-existent or known/believed to give poor predictions. –The assumptions made represent strong simplifications. –Data are not available, or are unreliable. –There is lack of agreement/consensus among experts. Minor uncertainty: All of the following conditions are met: –The phenomena involved are well understood; the models used are known to give predictions with the required accuracy. –The assumptions made are seen as very reasonable. –Much reliable data are available. –There is broad agreement among experts. Moderate uncertainty: Conditions between those characterising significant and minor uncertainty, e.g.: –The phenomena involved are well understood, but the models used are considered simple/crude. –Some reliable data are available. 1. Identify uncertainty factors a) Related to ageing phenomena/processes b) Related to assumptions in the traditional analyses 2. Evaluate treatment in traditional analysis 3. Assess and classify degree of sensitivity 4. Assess and classify degree of uncertainty 5. Assess and classify manageability 6. Suggest uncertainty reducing measures

16 Manageability assessment Significant manageability: Uncertainty can be reduced to “Low” through a set of measures that are considered cost-effective Moderate manageability: Uncertainty can be reduced to “Moderate” through a set of measures that are considered cost- effective Low manageability: No (cost-effective) measures can be identified 1. Identify uncertainty factors a) Related to ageing phenomena/processes b) Related to assumptions in the traditional analyses 2. Evaluate treatment in traditional analysis 3. Assess and classify degree of sensitivity 4. Assess and classify degree of uncertainty 5. Assess and classify manageability 6. Suggest uncertainty reducing measures