A comparative study between the system reliability evaluation methods: case study of mining dump trucks

Abstract

AbstractThe shovel-truck system is a widely used technique for haulage systems in surface mining operations. However, predicting the failure patterns of complex systems requires accurate failure prediction techniques. In this study, several major system reliability evaluation groups, including non-parametric, parametric, and semi-parametric methods, are investigated, and their effectiveness is compared to identify the best group for predicting the failure patterns of complex systems such as mining dump trucks, which operate in harsh environments. A historical dataset of time to failure (TTF) and maintenance data was collected. Then, the system’s reliability was evaluated using the major TTF data analysis methods. The findings demonstrated that all the major system reliability evaluation groups produced similar curves; however, the semi-parametric method outperformed the other methods. This result underscores that this system reliability evaluation group is the most effective method for complex systems. Also, it was found that the dump truck reliability dropped to 50% after 40 operation hours, demonstrating the critical importance of implementing preventive maintenance to enhance the system’s performance and ensure operation safety. In addition, this study provided an appropriate insight into the predictive methods and offered an accurate estimation of the failure pattern of complex systems, resulting in availability and productivity improvement.