Haul Truck Cycle Identification Using Support Vector Machine and DBSCAN Models

The haul trucks are one of the most often used assets in horizontal transport in underground copper ore mining. This haulage process has a cyclic form and in simple terms the machine drives from point A to point B, where its cargo box is respectively loaded and dumped. What is most important in its basic performance assessment is to identify each cycle and its parametrization in terms of total duration, idling, fuel consumption, and driving speed. In the literature, we can find a few similar works but the majority of them is based on a poorly available hydraulic pressure signal of the actuator in cargo box unloading system or braking system pressure signal. Unfortunately, all of them are not robust and unreliable in real, noisy signals. For this reason, searching for an innovative new concept of the solving problem seems right in this state. This paper describes the new method of the operation cycles identification for underground haul trucks, which is based on multidimensional techniques of operational data analysis using machine learning. The leading idea consists of three parts: searching characteristic non-hydraulic values in signals which correspond to cycles, identifying distinctive periods in haulage process and splitting signal into cycles accordingly. In the first step, the data mining techniques are used to find significant variables, afterwards SVM classifying model identifies unloadings, which are then applied to cluster data by DBSCAN algorithm. The whole process is presented on haul trucks real data from KGHM Lubin mine.