Multidimensional Data Segmentation Based on Blind Source Separation and Statistical Analysis

Horizontal transport in underground copper ore mines mainly consists of LHD machines (loaders, haulers ) and belt conveyors. One of the most crucial mining issues for assessment of efficiency of production is identification of operation cycles of haulage machines. In the literature one can find procedure based on analyzing of pressure signal variability developed for loader (Polak et al Identification of loading process based on hydraulic pressure signal pp 459–466, 2016, Stefaniak et al An effectiveness indicator for a mining loader based on the pressure signal measured at a bucket’s hydraulic cylinder 15, pp 797–805 [6, 7]). The algorithm allows to identify partial operations of loader cycles like: loading, haulage and return to mining face. For haulers this task can seem to be very easy to solve—machines are driving from point A to point B. Nevertheless, when we take into account harsh and specific conditions of underground mine, the problem remains very hard to solve using classical methods based on single variable and if-then-else rules. In most cases, those methods are not robust enough due many random factors (logistical, human factors, work organisation with loaders etc.). In this paper, we propose some kind of data fusion approach to recognition of partial hauler operations. Our method is based on blind source separation approach with particular focus on independent component analysis technique that uses JADE algorithm based on joint approximate diagonalization of eigenmatrices. Obtained components allow for easy segmentation of the signals.