Forecasting of time-to-failure is an important aspect of a mining machine for the performance assessment, fault detection and schedule maintenance. The knowledge of failure time allows more defined arrangement of preventive maintenance. Traditional methods, including lifetime distribution models, fault tree analysis and Markov models, have a limitation of assuming a specific statistical distribution function to fit the failure time data. In this study, a hybrid data-driven method using neural network and genetic algorithm is proposed to forecast failure time. The forecasting model was developed using neural network algorithm and all the neural network parameters, i.e. input nodes, hidden nodes and the learning algorithms, are selected automatically using the genetic algorithm. The developed model was validated using the failure data of a mining machine. A case study was conducted investigating a load-haul-dump machine (LHD) in the mining industry. Failure historical data for the LHD machine were collected, and cumulative failure time was calculated for time-to-failure forecasting. Study results demonstrate that the developed model performs satisfactory in the prediction of next failure time. A comparative study reveals that the proposed method performs better than existing methods.

Open pit mine production scheduling is a computationally expensive large-scale mixed-integer linear programming problem. This research develops a computationally efficient algorithm to solve open pit production scheduling problems under uncertain geological parameters. The proposed solution approach for production scheduling is a two-stage process. The stochastic production scheduling problem is iteratively solved in the first stage after relaxing resource constraints using a parametric graph closure algorithm. Finally, the branch-and-cut algorithm is applied to respect the resource constraints, which might be violated during the first stage of the algorithm. Six small-scale production scheduling problems from iron and copper mines were used to validate the proposed stochastic production scheduling model. The results demonstrated that the proposed method could significantly improve the computational time with a reasonable optimality gap (the maximum gap is 4%). In addition, the proposed stochastic method is tested using industrial-scale copper data and compared with its deterministic model. The results show that the net present value for the stochastic model improved by 6% compared to the deterministic model.