The main innovations proposed for development and implementation within the framework of the project, assessment of their fundamental novelty and competitiveness, compliance with environmental and other indicators, as well as the requirements of international standards. The main innovation of the project is to create more accurate methods and algorithms for identifying and predicting abnormal situations in the subsystems of remote technical objects (spacecraft, UAVs) using ensembles of artificial neural networks. The practical significance lies in the fact that the use of the developed methods and algorithms for the implementation of high-performance software and hardware systems will significantly reduce the time spent on processing incoming telemetry data, and especially on training classifiers of spacecraft states. An effective solution can be the development of a neural network software system for identifying the state of onboard objects and spacecraft subsystems based on modeling their behavior and intelligent processing of telemetric data.