摘要：The article describes the use of digital twins in socio-economic processes using the example of predictive asset maintenance management. For this, the architecture of a distributed forecasting information system is proposed that collects data from digital twins and provides them with a pre-trained neural network model to obtain forecasts about the need for predictive maintenance. The article discusses two types of forecasts - about the remaining useful life and the possible failure of an asset in the considered time window. Computational experiments have been carried out, confirming that the proposed neural network model allows, due to the simultaneous training of solving two problems, to obtain more accurate forecasts than models trained to solve one problem.
其他摘要：The article describes the use of digital twins in socio-economic processes using the example of predictive asset maintenance management. For this, the architecture of a distributed forecasting information system is proposed that collects data from digital twins and provides them with a pre-trained neural network model to obtain forecasts about the need for predictive maintenance. The article discusses two types of forecasts - about the remaining useful life and the possible failure of an asset in the considered time window. Computational experiments have been carried out, confirming that the proposed neural network model allows, due to the simultaneous training of solving two problems, to obtain more accurate forecasts than models trained to solve one problem.
