Study on theory and methods of payload online estimation for cable shovels

Abstract

Cable shovels are one of the most widely used machines in open-pit mining industry. Precise estimation of payload can effectively avoid overload and underload for each dump, significantly improve the working performance of cable shovels and reduce the maintenance costs of haul trucks. In this research, an algorithm based on static analysis for payload online estimation was established, which took hoist force, crowd force and geometrical parameters of the working device as input variables for the situation when both hoist and crowd motors were hovering during the digging process. More specifically, the torque balance function was applied for payload calculation on the basis of the kinematic model built for evaluating the length of different force arms when the dipper handle stayed in different positions. In order to verify the efficiency of the payload online estimation algorithm, six different working positions and four different fullness states of the dipper were chosen for testing. And a testing system consisting of a 1/35 scale model of cable shovel, software of LabVIEW, hardware of cRIO and multi-types of sensors was designed and set up. Testing results show that the relative error between the theoretically calculated value and the real weight of payload is averagely less than 6% when the dipper was 40% fully filled and averagely less than 3% when the fullness reached 100%, which has proved the efficiency of the payload online estimation theory and methods established in this research.