541
Excessive failure occurrences cause frequent interruption in production and deterioration of
system components before their expected useful lifetimes. The simulation was performed up
to an inspection interval of 200 h. The generated equations can be used to make a forecasting
for higher inspection intervals.
The simulation results also help to understand sensitivity of subsystems to the changing
inspection intervals. Figure 6 presents a representative graph for the subsystems of Shovel 3.
Inspection interval of 120 h was taken as a reference point; and increase or decrease in the
total corrective maintenance times for different inspection intervals were shown comparatively
in the figure. It is concluded from the graph that corrective maintenance profiles of SHA and
SHE are the most and the least sensitive to the changes in inspection intervals, respectively.
4 CONCLUSION
Effectiveness of a maintenance policy reserves a high uncertainty stemming from the stochastic lifetime behaviors of components and subsystems in the maintained systems. In addition, what to implement in planned and unplanned activities may change the financial and
operational benefit of the maintenance policy. This paper describes a simulation model that
integrates corrective, preventive and opportunistic maintenance to the stochastic repairing
and operating behavior of a working system. Once the integration was achieved, the model
was tested for different inspection intervals since inspections are critical in mines especially
for operationally important loading and hauling machines. In this basis, the model was tested
for three shovels. Operating and repairing stochasticity in the shovel subsystems was introduced to the model for a better understanding of system behavior. The simulation outputs
were used to reveal the sensitivity of subsystems to the changeable inspection intervals. In a
sample sensitivity graph plotted for Shovel 3, it was seen that SHA shows the highest sensitivity to inspection intervals where it is the least for SHE. Effect of opportunistic maintenance
to the overall maintenance cost will be considered in a future study.
REFERENCES
Gölbaşı, O. & Demirel, N. 2017. A cost-effective simulation algorithm for inspection interval optimization: An application to mining equipment. Computers and Industrial Engineering, 113, 525–540.
https://doi.org/10.1016/j.cie.2017.09.002.
Roy, S.K., Bhattacharyya, M.M., & Naikan, V.N.A. 2001. Maintainability and reliability analysis of
a fleet of shovels. Mining Technology, 110(3), 163–171. https://doi.org/10.1179/mnt.2001.110.3.163.
Figure 6. Sensitivity of total corrective maintenance time of shovel 3 subsystems to inspection interval.
__ o
Time betwM!n Inspections (h)
- -o--SHA - ·A-· SHC - a- -SHD -
sHE ·-·D-- SHH - - OTH
Excessive failure occurrences cause frequent interruption in production and deterioration of
system components before their expected useful lifetimes. The simulation was performed up
to an inspection interval of 200 h. The generated equations can be used to make a forecasting
for higher inspection intervals.
The simulation results also help to understand sensitivity of subsystems to the changing
inspection intervals. Figure 6 presents a representative graph for the subsystems of Shovel 3.
Inspection interval of 120 h was taken as a reference point; and increase or decrease in the
total corrective maintenance times for different inspection intervals were shown comparatively
in the figure. It is concluded from the graph that corrective maintenance profiles of SHA and
SHE are the most and the least sensitive to the changes in inspection intervals, respectively.
4 CONCLUSION
Effectiveness of a maintenance policy reserves a high uncertainty stemming from the stochastic lifetime behaviors of components and subsystems in the maintained systems. In addition, what to implement in planned and unplanned activities may change the financial and
operational benefit of the maintenance policy. This paper describes a simulation model that
integrates corrective, preventive and opportunistic maintenance to the stochastic repairing
and operating behavior of a working system. Once the integration was achieved, the model
was tested for different inspection intervals since inspections are critical in mines especially
for operationally important loading and hauling machines. In this basis, the model was tested
for three shovels. Operating and repairing stochasticity in the shovel subsystems was introduced to the model for a better understanding of system behavior. The simulation outputs
were used to reveal the sensitivity of subsystems to the changeable inspection intervals. In a
sample sensitivity graph plotted for Shovel 3, it was seen that SHA shows the highest sensitivity to inspection intervals where it is the least for SHE. Effect of opportunistic maintenance
to the overall maintenance cost will be considered in a future study.
REFERENCES
Gölbaşı, O. & Demirel, N. 2017. A cost-effective simulation algorithm for inspection interval optimization: An application to mining equipment. Computers and Industrial Engineering, 113, 525–540.
https://doi.org/10.1016/j.cie.2017.09.002.
Roy, S.K., Bhattacharyya, M.M., & Naikan, V.N.A. 2001. Maintainability and reliability analysis of
a fleet of shovels. Mining Technology, 110(3), 163–171. https://doi.org/10.1179/mnt.2001.110.3.163.
Figure 6. Sensitivity of total corrective maintenance time of shovel 3 subsystems to inspection interval.
__ o
Time betwM!n Inspections (h)
- -o--SHA - ·A-· SHC - a- -SHD -
sHE ·-·D-- SHH - - OTH