Optimizing replacement time for mining shovel teeth using reliability analysis and Markov chain Monte Carlo simulation

Abstract

Purpose

The purpose of this paper is twofold: an approach is proposed to determine the optimum replacement time for shovel teeth; and a risk-quantification approached is developed to derive a confidence interval for replacement time.

Design/methodology/approach

The risk-quantification approach is based on a combination of Monte Carlo simulation and Markov chain. Monte Carlo simulation whereby the wear of shovel teeth is probabilistically monitored over time is used.

Findings

Results show that a proper replacement strategy has potential to increase operation efficiency and the uncertainties associated with this strategy can be managed.

Research limitations/implications

The failure time distribution of a tooth is assumed to remain “identically distributed and independent.” Planned tooth replacements are always done when the shovel is not in operation (e.g. between a shift change).

Practical implications

The proposed approach can be effectively used to determine a replacement strategy, along with the level of confidence level, for preventive maintenance planning.

Originality/value

The originality of the paper rests on developing a novel approach to monitor wear on mining shovels probabilistically. Uncertainty associated with production targets is quantified.

Citation

Sembakutti, D., Ardian, A., Kumral, M. and Sasmito, A.P. (2018), "Optimizing replacement time for mining shovel teeth using reliability analysis and Markov chain Monte Carlo simulation", International Journal of Quality & Reliability Management, Vol. 35 No. 10, pp. 2388-2402. https://doi.org/10.1108/IJQRM-09-2017-0187