Reliability estimation using a genetic algorithm-based artificial neural network: An application to a load-haul-dump machine

In this study, a neural network-based model for forecasting reliability was developed. A genetic algorithm was applied for selecting neural network parameters like learning rate (η) and momentum (μ). The input variables of the neural network model were selected by maximizing the mean entropy value. The developed model was validated by applying two benchmark data sets. A comparative study reveals that the proposed method performs better than existing methods on benchmark data sets. A case study was conducted on a load-haul-dump (LHD) machine operated at a coal mine in Alaska, USA. Past time-to-failure data for the LHD machine were collected, and cumulative time-to-failure was calculated for reliability modeling. The results demonstrate that the developed model performs well with high accuracy (R2 = 0.94) in the failure prediction of a LHD machine.

► We developed a genetic algorithm-based neural network reliability model.
► The automatic input variables is improved the performance of the model.
► The genetic algorithm is applied for selecting the learning parameters of neural network.
► Our algorithm is performed better than existing algorithms in two bench mark data sets.
► The application on load-haul-dump machine shows satisfactory results in comparison with other methods.