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Cost Comparison Function

Cost Comparison Function. Determining the benefits of an improved decision model over an existing one. CBM effectiveness – How “good” is the condition data?. CBM effectiveness is related , to how “good” the condition data is. That is, to what degree:

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Cost Comparison Function

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  1. Cost Comparison Function Determining the benefits of an improved decision model over an existing one

  2. CBM effectiveness – How “good” is the condition data? • CBM effectiveness is related, to how “good” the condition data is. That is, to what degree: • it reflects internal degradation in the item, and / or • it measures the accumulated external stress imposed on the item.

  3. CBM effectiveness also depends on • The ratio of the average cost of a preventive action to the average economic consequences of failure. • Where: • C=cost of a proaction • K=added cost due to failure • EF = histories ended by failure • ES = histories ended by proaction (suspension)

  4. CBM decision modeling depends on: How well the life ending events: • Failure (either PF or FF), or • Suspension have been discriminated in the CMMS.

  5. Therefore… To improve CBM, we need to improve • Feature extraction – select features that reflect internal or external influence. • Perform CBM on items with a high C/C+K ratio. • Discriminate between ending event types in the CMMS. How good is our CBM performance? “We can’t improve what we can’t measure.” How do we measure CBM Performance?

  6. Open, the working model (Transmission_WMOD.mdb) database

  7. 2. Modeling, Select Current Model

  8. 3. Oil Anal, FePb, OK

  9. 4. Modeling

  10. 5. Decision model

  11. 6. Cost Comparison

  12. 7. Exclude histories: We may apply a time value prior to which high monitored results should not trigger an EXAKT alarm. (See Min. Prev. Repl. Time.) By checking checkbox "Apply to All Histories", the time value will be replicated as you click on any other row. OK

  13. 8. Edit individual histories that were not excluded in the previous step. We may now exclude any record (for example, those with doubtful or missing readings) from any history by changing "-1" to "0". OK. “-1” means the entire history was not excluded in previous step.

  14. 9. Examine the "Summary of Decision Model Parameters". The column headings Min. Prev. Repl. Time and Reg. Maint. Int. are explained in the Lexicon

  15. 10. Examine the "Summary of Events and Decided Histories". “Current”: What actually occurred. Of the 13 actual histories in the sample 6 failed, 3 were replaced, and 4 are “undecided” – that is, at this time we do not know whether they will eventually fail or be preventively replaced. (At present they are still operating). • EXAKT applied: When the EXAKT policy is applied retroactively to the data set, • 1 history would have ended having failed, • 6 would have been preventively replaced, and • 6 would have been undecided . we may conclude that the number of failures would have been significantly reduced, but at what cost? Fitted EXAKT applied: See speaker notes.

  16. 11. Examine Table A: Summary of Cost Comparison with Current Policy (undecided histories counted) • EXAKT applied: The cost of the policy obtained from applying the optimal model retroactively to the sample. • Fitted EXAKT applied: The curve of the EXAKT decision chart is fitted to the actual data; so as to minimize “average” realized cost. • EXAKT: The theoretical “expected” cost effectiveness of the EXAKT model. • Replace at failure: The policy of not using any proactive (neither scheduled nor on-condition) maintenance.

  17. 11. Examine Table B: Summary of Cost Comparison with Current Policy (undecided histories not counted) Table B provides the other extreme assumption. While Table A assumed that histories that are at present incomplete will have been preventively replaced by the proposed decision model, Table B simply ignores the incomplete histories. One may consider the assumptions of A and B as defining the envelope of possibilities of future performance of the model. If both provide satisfactory results, we may confidently apply the model going forward.

  18. Summary • The above analysis provides a way to judge the potential of a proposed CBM policy. • It uses various sets of calculations to probe the robustness of the proposed model • It is a tool that a maintenance engineer uses to gain a degree of comfort by arriving at similar numbers using calculations at both edges of the envelope of possible solutions.

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