Neural quality management model for express diagnostics of helicopter engines

The purpose of the work is to improve the quality of maintenance and maintain the operational reliability of helicopter equipment. Aircraft are one of the most complex technical systems created and used by humans. The process of aircraft operation is accompanied by failures and malfunctions. In reliability theory, a failure is a case when the operation process is interrupted for the purpose of repairing an object, and a malfunction consists in the fact that some technical indicators go beyond the permissible thresholds regulated by the manufacturer. To maintain the reliability of the helicopter, diagnostic systems based on digital principles are used. The paper considers the process of formalizing diagnostic technology on a neural network platform, Big Data and digital twins. The neural diagnostic model is integrated into the Digital Twin system.