In mining, grade control generally focuses on blasthole sampling and the estimation of ore control block models with little or no attention given to how the materials are being excavated from the ground. In the process of loading trucks, the underlying variability of the individual bucket load will determine the variability of truck payload. Hence, accurate material movement demands a good knowledge of the excavation process and the buckets’ interaction with the environment. However, equipment frequently goes into off-nominal states due to unexpected delays, disturbances or faults. The large amount of such disturbances causes information loss that reduces the statistical power and biases estimates, leading to increased uncertainty in the production. A reliable method that inferences the missing knowledge about the interaction between the machine and the environment from the available data sources, is vital to accurately model the material movement.
In this study, a twostep method was implemented that performed unsupervised clustering and then predicted the missing information. The first method is Density-based spatial clustering of applications with noise (DBSCAN) based spatial clustering which divides the diggers’ and buckets’ positional data into connected loading segments. Clear patterns of segmented bucket dig positions were observed. The second model utilised Gaussian process regression which was trained with the clustered data and the model was then used to infer the mean locations of the test clusters. Bucket dig locations were then simulated at the inferred mean locations for different durations and compared against the known bucket dig locations. This method was tested at an open pit mine in the Pilbara region of Western Australia. The experimental results demonstrate the advantage of the proposed method in inferencing the missing information of bucket-environment interactions and therefore enables miners to continuously track the material movement.