Development of a Markov model for production performance optimisation. Application for semi-automatic and manual LHD machines in underground mines

Abstract

This paper compares three ways to operate a load haul dump (LHD) machine, manual operation, automatic operation (fleet operation) and semi-automatic operation, to find the best operating mode. In a fault tree analysis, different failures are classified and analysed, but the way to recover from certain states is not accounted for, which is something a Markov model can handle. The paper is based on the analysis of real data from an underground mine. A Markov model has been built for mining application and it is shown that a semi-automatic LHD has the highest probability of being in a productive state since it has the advantage of changing operating modes (manual and automatic) depending on the need and situation. Hence, the semi-automatic LHD is the best choice from an operational point of view. The paper fills a gap in the literature on manual vs. automatically operated LHDs by providing a new way of evaluating the operating mode of LHDs using Markov modelling, while considering the operating environment.

Keywords:

• Markov process
• load haul dump (LHD) machine
• reliability
• automation

Acknowledgements

The Swedish Foundation for Strategic Research (SSF), ProViking and Sandvik Mining and Construction are acknowledged for their financial support. Professor Nava Raj Karki is acknowledged for contributing with helpful comments.