articles

The results of studies on the development of the structure of an intelligent model for managing the risks of violation of the characteristics of electromechanical devices in a multi-stage system for processing ore raw materials are presented. Such devices are involved in all cycles of the technological process, so the assessment of this risk for them is an urgent task. A method for assessing such risks is proposed, which is based on the assessment of the useful life of equipment, performed on the basis of the prediction of characteristics by a deep recurrent neural network, with further generalization of the results of such an assessment in a fuzzy inference block. Recurrent neural networks with long short-term memory were used, which are one of the most powerful tools for solving time series regression problems, including predicting their values for long intervals. The use of deep neural networks to predict the characteristics of electromechanical devices made it possible to obtain a high prediction accuracy, which made it possible to apply a relatively less accurate recurrent least squares method for the iterative process of estimating the useful life of equipment. This approach made it possible to build a computational evaluation process with its constant refinement as new results of measurements of the characteristics of electromechanical devices become available. The results of a model experiment with a software implementation of the proposed method, performed in the MatLab 2021a environment, are presented, which showed the consistency of the program modules and obtaining a risk assessment result that is consistent with the expected dynamics of its change.