Abstract
Maintenance and physical asset managers often have to decide when a major asset needs to be replaced. The main objective of this study was to develop a methodology to determine the optimum replacement age of heavy mobile equipment that is close to the end of its life. The study was conducted on an old electric rope shovel used at a surface coal mining operation. The failure impact and failure probability estimates of components were obtained from subject matter experts through Delphi analyses. A stochastic-and-parametric-estimation modelling solution was developed to perform quantitative risk analyses using their inputs. The solution calculated the expected loss of the rope shovel as a function of machine-age within a 90 per cent confidence interval. The study demonstrated that the optimum replacement age of heavy mobile equipment can be obtained by modelling the expected losses due to the failure of critical end-of-life components, taking into account the uncertainty in data obtained from subject matter experts.
Highlights
With expensive capital investments such as electric rope shovels used in mining operations, managers are concerned with the question, “When should we replace the asset?” The engineers in the organisation usually want to replace assets as soon as the risk of severe unplanned equipment failures increases
The engineers speak in engineering terms and the financial managers in financial terms, and apples are compared with oranges
A stochastic-and-parametricestimation modelling tool was developed to perform quantitative risk analyses using the inputs from the subject matter experts
Summary
1.1 BackgroundWith expensive capital investments such as electric rope shovels used in mining operations, managers are concerned with the question, “When should we replace the asset?” The engineers in the organisation usually want to replace assets as soon as the risk of severe unplanned equipment failures increases. The engineers speak in engineering terms (equipment condition) and the financial managers in financial terms (cost of capital), and apples are compared with oranges. Both engineers and financial managers need the same consolidated evidence to decide on the best time to replace the equipment. A stochastic-and-parametricestimation modelling tool was developed to perform quantitative risk analyses using the inputs from the subject matter experts. The critical EOL components have a high impact on the machine’s reliability, meaning that if these components become unreliable, the HME’s performance is severely reduced. In this case, 31 EOL components were identified and evaluated.
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