Risk modelling of heavy mobile equipment to determine optimum replacement ages

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.

Fisiese bate- en instandhoudingsbestuurders moet dikwels besluit wanneer om ’n groot bate te vervang. Die hoofdoel van hierdie studie was om ’n metodologie te ontwikkel om die optimale vervangtyd van swaar mobiele toerusting naby die einde van sy leeftyd te bepaal. Die studie was uitgevoer op ’n ou elektriese tougraaf wat by ’n oopgroef steenkool myn aangewend word. Die falings impak en falings waarskynlikhede van komponente is verkry vanaf onderwerpkenners deur middel van Delphi analises. ’n Stogastiese-en-parametriese-beraming modellering oplossing was ontwikkel om kwantitatiewe risiko analises met hul insette te doen. Die oplossing het die verwagte verliese van die tougraaf as ’n funksie van ouderdom bereken binne ’n 90 persent vertrouensinterval. Die studie het getoon dat die optimum vervangingsouderdom van swaar mobiele toerusting bepaal kan word deur die verwagte verliese as gevolg van falings van kritieke komponente naby die einde van hul leeftyd te modelleer, met inagname van die onsekerheid in data verkry vanaf onderwerpkenners.