Classifying dragline swing dependencies using coincident limit graphs

Abstract

Dragline productivity is strongly influenced by swing cycle times, comprising between 60–70% of all dragline operational time. The ability to classify and report on dragline swing cycles according to their limiting motion is essential for identifying performance losses and deriving continuous improvements. This paper presents an algorithm for the automatic classification of dragline cycle dependencies. The algorithm adopts a simple graphical approach, based on coincident limits defined by the locus of points where the swing and hoist motors work at full capacity for equivalent time periods. The algorithm is capable of distinguishing three cycle dependencies; swing, hoist, and drag limited cycles. The coincident limit algorithm was applied on a dragline at a coal mine in Queensland’s Bowen Basin. A data set of 200 000 cycles consisted of: 70% swing limited cycles; 27% hoist limited cycles and 3% drag limited cycles. The coincident limit algorithm permits an equivalent swing angle to be calculated for draglines in hoist limited situations. This facilitates the possibility of measuring dragline work as BCMs multiplied by the equivalent swing angle. This measure could potentially be applied to benchmark the performance of draglines in different operational contexts in which varied pit and block geometries influence the proportion of hoist limited cycles.