Bayesian Network Approach for Dragline Reliability Analysis: a Case Study

Abstract

Draglines are extensively used in Indian mines. A dragline has more than hundreds of components, and it is complex in design. This study involves the evaluation of the reliability of a draglines system using a Bayesian network (BN) model mapped from a fault tree. Based on the BN inference, the reliability estimation, the diagnosis, and the sensitivity analysis are performed. In this paper, the overall reliability of the dragline is estimated as well as the contribution of the subsystems or components in the overall reliability evaluation is presented. The results showed that the three subsystems of the dragline, namely, the dragging mechanism, electrical auxiliary subsystem, and swing mechanism, have the lowest reliability (82.17%, 87.98%, and 91.30%, respectively) after an hour of operation. The overall reliability at the first hour of machine operation is estimated to be 62.03%. The study may provide a reference for future work related to the dragline machine’s reliability design and maintenance. © 2023, Society for Mining, Metallurgy & Exploration Inc.