On the Optimization of Condition-Based Maintenance Decisions Andrew Jardine
Where it started - 1982
Estimated Hazard Rate at Removal Number Flight Hours Fe Cr Hazard Rate 1 11770 5 6 0.043 2 11660 2 6 0.012 3 8460 12 2.4 0...
From Data to Hazard
From Data to Hazard PHM
Optimal Policy: Optimal Hazard Level Hazard Plot Cost Plot Ignore Hazard Replace at failure only minimal cost optimal haza...
Optimal Decision Chart
Condition-Based Maintenance www.dictuctribologia.cl
Irving Pulp and Paper: Executive Summary <ul><li>Analysis of Goulds 3175L Pumps </li></ul><ul><li>Bearings Vibration Data ...
Using EXAKT Decision Chart
Summary: Principle of CBM Optimization Age Data Condition- Monitoring Data Maintenance Decision Hazard Model Transition Mo...
A New Development rooted in  CBM optimization - 2011 Risk factors and optimization  model for breast cancer screening
Background <ul><li>Interdisciplinary collaborative project involving natural sciences or engineering and health sciences. ...
Motivation <ul><li>In Canada (population 32 million) 1 in 9 women expected to develop breast cancer; 1 in 28 will die. </l...
Machine Failure vs. Cancer Formation - risk of failure - hazard rate - age - oil - vibration - temperature - age - family ...
Machine Inspection vs. Mammography <ul><li>Periodic/non-periodic inspections to detect fault/failure </li></ul><ul><li>How...
Project Objectives <ul><li>Risk factors of breast cancer and risk prediction models </li></ul><ul><li>Optimization of mamm...
Acknowledgements <ul><li>Colleagues at C-MORE (Research staff, students and post-doctoral Fellows) </li></ul><ul><li>Detai...
The MORE Consortium Members
Thank you Andrew Jardine
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Andrew Jardine Universidad de Toronto

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Jardine egaf10

  1. 1. On the Optimization of Condition-Based Maintenance Decisions Andrew Jardine
  2. 2. Where it started - 1982
  3. 3. Estimated Hazard Rate at Removal Number Flight Hours Fe Cr Hazard Rate 1 11770 5 6 0.043 2 11660 2 6 0.012 3 8460 12 2.4 0.0071 4* 12630 8 1 0.0014 5 7710 8 0 0.00094 6* 9240 2 3 0.00029 7* 5660 10 1 0.00020 8* 7190 2 2.5 0.000073 * Doubtful Removal Number Flight Hours Fe Cr Hazard Rate 1 11770 5 6 0.043
  4. 4. From Data to Hazard
  5. 5. From Data to Hazard PHM
  6. 6. Optimal Policy: Optimal Hazard Level Hazard Plot Cost Plot Ignore Hazard Replace at failure only minimal cost optimal hazard Age Hazard Hazard Cost/unit time Optimal Hazard level
  7. 7. Optimal Decision Chart
  8. 8. Condition-Based Maintenance www.dictuctribologia.cl
  9. 9. Irving Pulp and Paper: Executive Summary <ul><li>Analysis of Goulds 3175L Pumps </li></ul><ul><li>Bearings Vibration Data </li></ul><ul><li>56 vibration measurements provided by accelerometer </li></ul><ul><li>Using <EXAKT>: </li></ul><ul><li>2 measurements significant </li></ul><ul><li>A Check: </li></ul><ul><li>Had <EXAKT> model been applied to previous histories </li></ul><ul><li>Savings obtained = 33 % </li></ul>
  10. 10. Using EXAKT Decision Chart
  11. 11. Summary: Principle of CBM Optimization Age Data Condition- Monitoring Data Maintenance Decision Hazard Model Transition Model Cost and Availability Model Remaining Useful Life Engineering Judgment Supplied by user Software engine Intermediate result Final result
  12. 12. A New Development rooted in CBM optimization - 2011 Risk factors and optimization model for breast cancer screening
  13. 13. Background <ul><li>Interdisciplinary collaborative project involving natural sciences or engineering and health sciences. </li></ul><ul><li>Principal Investigator: Prof. Andrew Jardine </li></ul><ul><li>Co-investigators: </li></ul><ul><li>Prof. Bart Harvey Dalla Lana School of Public Health </li></ul><ul><li>Prof. Anthony Miller Dalla Lana School of Public Health </li></ul><ul><li>Prof. Jeffrey Hoch Department of Health Policy, </li></ul><ul><li>Management and Evaluation </li></ul><ul><li>Prof. Terrence Sullivan Cancer Care Ontario </li></ul>
  14. 14. Motivation <ul><li>In Canada (population 32 million) 1 in 9 women expected to develop breast cancer; 1 in 28 will die. </li></ul><ul><li>445 new diagnosed cases and 100 deaths every week . </li></ul><ul><li>Minimizing chances of breast cancer by knowing its risk factors. </li></ul><ul><li>Some already known factors: age, race, family history, starting age and frequency of screening, etc. </li></ul><ul><li>Mammography screening brings forward the time of diagnosis, so potentially decreases mortality. </li></ul>
  15. 15. Machine Failure vs. Cancer Formation - risk of failure - hazard rate - age - oil - vibration - temperature - age - family history - race - genetics <ul><li>risk of cancer </li></ul><ul><li>incidence rate </li></ul><ul><li>mortality rate </li></ul>Prognostic Models Condition monitoring Personal and life style factors
  16. 16. Machine Inspection vs. Mammography <ul><li>Periodic/non-periodic inspections to detect fault/failure </li></ul><ul><li>How often to inspect a machine? </li></ul><ul><li>Mammography for early detection of cancer </li></ul><ul><li>How often to get a mammogram? </li></ul>
  17. 17. Project Objectives <ul><li>Risk factors of breast cancer and risk prediction models </li></ul><ul><li>Optimization of mammography screening </li></ul>
  18. 18. Acknowledgements <ul><li>Colleagues at C-MORE (Research staff, students and post-doctoral Fellows) </li></ul><ul><li>Details at www.mie.utoronto.ca/cmore </li></ul><ul><li>Companies that support our research program (see next slide) </li></ul>
  19. 19. The MORE Consortium Members
  20. 20. Thank you Andrew Jardine

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