ABSTRACT
Self-propelled Mining Machines (LHD—Load Haul Dump, Haul Truck) are the basic technical resource used in copper ore mines in KGHM PM S.A. Machine operation costs: depreciation, service, consumables, remuneration of operators, etc., account for over 30% of the technical costs of copper ore production. To optimize the cost machines and processes are monitored. The aim of the article is to select variables available in the Haul Truck monitoring system for operational regimes identification. The development of indicators for evaluation of the organization of work in the production process, e.g. number of machine cycles, average cycle time, deviations from the average cycle time, starting/finishing of machine operation for work shift, etc. may be considered as the first step to optimize the cost of the process. A method for counting work cycles for LHD loaders has been already developed, using the pressure signal from the actuator in the hydraulic system. However, in the case of Haul Truck, the nature of data is different and the new algorithm is needed. Moreover, in practice, there are limitations in the use of the pressure based indicator due to the loss of data caused by the pressure sensor’s susceptibility to mechanical damage. Based on these experiences, we will improve the method, thanks to the use of sensors that are not so susceptible to damage. The article will present the results of analyses regarding the possibility of using other registered physical variables (engine revolutions, instant fuel consumption, speed, the pressure in the braking system, etc.), which are much more reliable and resistant to interference.
