Optimizing mining hoist maintenance decisions through blending oil analysis and hazard modelling
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1 Optimizing mining hoist maintenance decisions through blending oil analysis and hazard modelling Miguel Sánchez Cía. Minera Doña Inés de Collahuasi, Chile Pedro Cancino, Dictuc Tribologia, Chile
2 Introduction This paper discusses work carried out at Minera Collahuasi to improve the existing oil analysis program currently undertaken for hoist systems. Current practice for monitoring the health of equipment is by examining trends in readings obtained from condition monitoring. Such an approach does not guarantee that the full information-value contained in the readings is captured.
3 About Minera Collahausi Shareholders. Production. Location. Fleet.
4 Methodology This paper reports a new approach that uses a statistical procedure, proportional-hazards modeling (PHM) to identify the key measurements that should be used to assess the true state of health of equipment. Once this is done, then economic considerations must be taken into account to establish the optimal maintenance decision which should be taken at the time of inspection.
5 Data Availability Within the mine s computer maintenance management system (CMMS) from MINCOM ELLIPSE there are histories of hoist lifetimes, including details of removals due to a preventative policy or failure. Costs associated with failure and preventive removals were also available. Oil analysis results from a fleet of 9 shovels with electric cable hoist systems were analyzed along with their respective failures and repairs over a six year period.
6 Life Cycle of a Component
7 Life Cycle of a Component Age Effect
8 Current Practice Age Effect Normal Warning Alarm Cr < >20 Cu < >100 Fe < >300 Si < >25
9 Summary: Principle of CBM Optimization Age Data Condition- Monitoring Data Maintenance Decision Supplied by user Software engine Intermediate result Final result Hazard Model Transition Model Remaining Useful Life Cost and Availability Model Engineering Judgment
10 Hazard Model Failures/op.hour Failures/km. Failures/cycle Failures/tonne Etc Contribution of age to hazard Contribution of condition information to hazard
11 Combined Data From All Histories / Data Cleaning Equipment System Interven1ons / Histories Installa1on Date Actual Component Life / Hours EOM Recomended Life / Hours Type of Events PA01 Hoist Middle ShaF Hoist Gear 09 may 00 0, B PA01 Hoist Middle ShaF Hoist Gear 14 abr , EF PA01 Hoist Middle ShaF Hoist Gear 15 abr 02 0, B PA01 Hoist Middle ShaF Hoist Gear 04 nov , EF PA01 Hoist Middle ShaF Hoist Gear 05 nov 02 0, B PA01 Hoist Middle ShaF Hoist Gear 30 oct , EF PA01 Hoist Middle ShaF Hoist Gear 30 oct , EF PA01 Hoist Middle ShaF Hoist Gear 31 oct 03 0, B PA01 Hoist Middle ShaF Hoist Gear 31 oct 03 0, B PA01 Hoist Middle ShaF Hoist Gear 15 jul , EF PA01 Hoist Middle ShaF Hoist Gear 16 jul 07 0, B PA01 Hoist Oil Change 15 oct ,0 OC PA01 Hoist Oil Change 22 nov ,0 OC PA01 Hoist Oil Change 13 oct ,0 OC PA01 Hoist Middle ShaF Hoist Gear 03 dic , EF PA01 Hoist Middle ShaF Hoist Gear 04 dic 09 0, B PA01 Hoist Middle ShaF Hoist Gear 01 ene , EF PA01 Hoist Middle ShaF Hoist Gear 01 ene , EF
12 Combined Data From All Histories / Data Cleaning Equipment System Interven1ons / Histories Installa1on Date Actual Component Life / Hours EOM Recomended Life / Hours Type of Events PA01 Hoist Middle ShaF Hoist Gear 09 may 00 0, B PA01 Hoist Middle ShaF Hoist Gear 14 abr , EF PA01 Hoist Middle ShaF Hoist Gear 15 abr 02 0, B PA01 Hoist Middle ShaF Hoist Gear 04 nov , EF PA01 Hoist Middle ShaF Hoist Gear 05 nov 02 0, B PA01 Hoist Middle ShaF Hoist Gear 30 oct , EF PA01 Hoist Middle ShaF Hoist Gear 30 oct , EF PA01 Hoist Middle ShaF Hoist Gear 31 oct 03 0, B PA01 Hoist Middle ShaF Hoist Gear 31 oct 03 0, B PA01 Hoist Middle ShaF Hoist Gear 15 jul , EF PA01 Hoist Middle ShaF Hoist Gear 16 jul 07 0, B PA01 Hoist Oil Change 15 oct ,0 OC PA01 Hoist Oil Change 22 nov ,0 OC PA01 Hoist Oil Change 13 oct ,0 OC PA01 Hoist Middle ShaF Hoist Gear 03 dic , EF PA01 Hoist Middle ShaF Hoist Gear 04 dic 09 0, B PA01 Hoist Middle ShaF Hoist Gear 01 ene , EF PA01 Hoist Middle ShaF Hoist Gear 01 ene , EF
13 Combined Data From All Histories / Data Cleaning Equipment System Interven1ons / Histories Installa1on Date Actual Component Life / Hours EOM Recomended Life / Hours Type of Events PA01 Hoist Middle ShaF Hoist Gear 09 may 00 0, B PA01 Hoist Middle ShaF Hoist Gear 14 abr , EF PA01 Hoist Middle ShaF Hoist Gear 15 abr 02 0, B PA01 Hoist Middle ShaF Hoist Gear 04 nov , EF PA01 Hoist Middle ShaF Hoist Gear 05 nov 02 0, B PA01 Hoist Middle ShaF Hoist Gear 30 oct , EF PA01 Hoist Middle ShaF Hoist Gear 30 oct , EF PA01 Hoist Middle ShaF Hoist Gear 31 oct 03 0, B PA01 Hoist Middle ShaF Hoist Gear 31 oct 03 0, B PA01 Hoist Middle ShaF Hoist Gear 15 jul , EF PA01 Hoist Middle ShaF Hoist Gear 16 jul 07 0, B PA01 Hoist Oil Change 11 sep ,0 OC PA01 Hoist Oil Change 15 oct ,0 OC PA01 Hoist Oil Change 13 nov ,0 OC PA01 Hoist Middle ShaF Hoist Gear 03 dic , EF PA01 Hoist Middle ShaF Hoist Gear 04 dic 09 0, B PA01 Hoist Middle ShaF Hoist Gear 01 ene , EF PA01 Hoist Middle ShaF Hoist Gear 01 ene , EF
14 Age Effect
15 How does data model hazard? Sample History History consists of: Beginning time Inspection results Ending time and reason significant covariates. Age Effect EQUIPMENT FAILURE
16 Combined data from all histories.
17 PHM Model for MSHG
18 Optimal Hazard Level C p = Total cost of preventive replacement C f = Total cost of failure replacement C p = cost of manpower + cost of a new gear US$ 0.25 M C f = cost of manpower + cost of a new gear + opportunity cost US$ 2.3 M
19 Decision Graph for MSHG of PA04 at hours or at hours. Z 00 = *Fierro *Magnesio *Molibdeno *Nickel *Sodio *Zinc
20 Conclusion The case study demonstrates the value of using the PHM technique to assist maintenance professionals to interpret condition data by identifying the key risk factors and their relative influence on the health of equipment. Economic considerations were then blended with the PHM risk model to identify the optimal decision chart.
21 Potential Savings The optimal decision model indicates that the implementation of a Condition Based Maintenance Strategy in these cases will result in more frequent preventive replacements in order to avoid costly failures. The study then indicates that the implementation of the decision chart would result in a cost reduction of 38%.
22 Acknowledgements The authors kindly acknowledge: Dr. Andrew Jardine Dr. Daming Lin Centre for Maintenance and Optimization & Reliability Engineering. Mr. Luis Barberá University of Sevilla. For their support during the data analysis.
23 Optimizing mining hoist maintenance decisions through blending oil analysis and hazard modelling Miguel Sánchez Cía. Minera Doña Inés de Collahuasi, Chile Pedro Cancino, Dictuc Tribologia, Chile
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