Abstract
This study proposes a method for diagnosing problems in truck ore transport operations in underground mines using four machine learning models (i.e., Gaussian naïve Bayes (GNB), k-nearest neighbor (kNN), support vector machine (SVM), and classification and regression tree (CART)) and data collected by an Internet of Things system. A limestone underground mine with an applied mine production management system (using a tablet computer and Bluetooth beacon) is selected as the research area, and log data related to the truck travel time are collected. The machine learning models are trained and verified using the collected data, and grid search through 5-fold cross-validation is performed to improve the prediction accuracy of the models. The accuracy of CART is highest when the parameters leaf and split are set to 1 and 4, respectively (94.1%). In the validation of the machine learning models performed using the validation dataset (1500), the accuracy of the CART was 94.6%, and the precision and recall were 93.5% and 95.7%, respectively. In addition, it is confirmed that the F1 score reaches values as high as 94.6%. Through field application and analysis, it is confirmed that the proposed CART model can be utilized as a tool for monitoring and diagnosing the status of truck ore transport operations.
Highlights
Because the productivity and profits of mines can vary greatly depending on the design and planning of the production process, optimal operation methods and equipment utilization strategies are needed to maximize productivity and equipment efficiency and minimize operating costs [1,2,3,4,5]
Salama and Greberg [15] performed a simulation of a loading-haulage-dumping machine (LHD) and a truck to optimize the number of trucks used in haulage operation in an underground mine
For the learning and validation of machine models, 33,435 truck travel time data were collected for 15 weeks using the mine production management system installed in the study area
Summary
Because the productivity and profits of mines can vary greatly depending on the design and planning of the production process, optimal operation methods and equipment utilization strategies are needed to maximize productivity and equipment efficiency and minimize operating costs [1,2,3,4,5]. Various mathematical decisions and deterministic and probabilistic simulation models have been proposed by researchers to establish an operational plan, such as optimizing the operational method and equipment allocation plan of the mine transport system and minimizing material handling costs [4,7,8,9,10,11,12,13]. Choi and Nieto [3] extended this to analyze the optimal transport path of a truck. They performed discrete event simulations of transport equipment and provided a function to visualize the simulation results. Park and Choi [17,18,19,20,21,22] developed GPSS/H-based programs and user-friendly programs to simulate truck-loader transport systems, considering various conditions such as fixed/real-time allocation, crusher capacity, and possibility of truck failure
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