Reliability assessment of dragline's subsystem using dynamic Bayesian network

Abstract

Draglines are very complex in design and consist of hundreds of components. Ensuring the high reliability of a dragline is essential for the economic sustainability of a surface mining project. This study proposes a methodology for the reliability assessment of the dragline's subsystem using the dynamic Bayesian network (DBN). The reliability of the dragging subsystem highly depends on the reliability of the drag brake, drag socket, and power failure. The dragging subsystem reliability is 84.29% at 1 hr. of machine operation. This study provides useful data for dragline maintenance planning and a reliability design. © 2024 Inderscience Enterprises Ltd.