Using deep learning methods for teaching neural network structure for solving problems of problems of predoctive analysis of equipment breakdowns

Abstract

Neural networks are successfully used for the synthesis of control systems for dynamic objects. Neural networks have a number of properties that determine the prospects of their use as an analytical apparatus of control systems. In the context of the problem under consideration, this is, above all, the ability to learn by example. The presence of large volumes of monitoring data, which presents interconnected measurements of both the inputs and outputs of the studied system, allows the neural network to be provided with representative training samples.