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Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
Online media
issn 2310-6018

The use of an artificial neural network in thermal diagnostics of the printed node of the on-board take-off control device of an aircraft

idUvaysov S.U. Chernoverskaya V.V.   Dang N.V.   Tuan N.V.  

UDC 621.396.69
DOI: 10.26102/2310-6018/2022.38.3.012

  • Abstract
  • List of references
  • About authors

Technical diagnostics and monitoring of an electronic device are integral parts of its life cycle since they help to assess not only the technical condition of components and modules in real time, but also make it possible to identify hidden defects that have arisen during the production or operation of the device, and make a forecast about the residual life of the product. It can be said that in the process of technical diagnostics, the reliability indicators of the device under study and the compliance degree of the embedded and implemented functionality are evaluated, which is inextricably linked with the qualitative characteristics of the product. Modern radio-electronic devices characterized by high circuit, structural and technological complexity require additional study of the existing diagnostic methods and the search for new approaches to increasing the resolution, reliability, and effectiveness of diagnostic procedures. In this area, achievements from the field of artificial intelligence, machine learning, and neural networks along with traditional, proven methods have been actively used recently. In addition, the use of modeling and computational experiment in design made it possible to combine design and diagnostic procedures, conduct diverse studies of the virtual twin of the device and make the necessary changes in a timely manner, thereby preventing the manifestation of negative effects in the finished product at the early stages of development even before the production of a prototype. The article presents the results of a study aimed at creating a thermal model of the designed node and developing an artificial neural network for recognizing structural defects of the device by its thermal field. In this research, specialized computer-aided design systems were actively employed, including engineering analysis and calculation tools, as well as the high-level Python programming language. The findings have a practical importance and can be utilized by developers of radio-electronic devices in order to achieve high reliability and operational characteristics of the product at all stages of its life cycle.

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Uvaysov Saygid Uvaysovich
Doctor of Technical Sciences Professor

WoS | Scopus | ORCID |

MIREA – Russian Technological University

Moscow, Russian Federation

Chernoverskaya Victoria Vladimirovna
Candidate of Technical Sciences, Associate Professor

eLibrary |

MIREA – Russian Technological University

Moscow, Russian Federation

Dang Nguyen Viet

MIREA – Russian Technological University

Moscow, Russian Federation

Tuan Nguyen Van

MIREA – Russian Technological University

Moscow, Russian Federation

Keywords: radio-electronic device, on-board device, control of aircraft take-off dynamics, thermal mode, thermal modeling, artificial neural network, fault database, computer-aided design system, technical diagnostics

For citation: Uvaysov S.U. Chernoverskaya V.V. Dang N.V. Tuan N.V. The use of an artificial neural network in thermal diagnostics of the printed node of the on-board take-off control device of an aircraft. Modeling, Optimization and Information Technology. 2022;10(3). Available from: https://moitvivt.ru/ru/journal/pdf?id=1218 DOI: 10.26102/2310-6018/2022.38.3.012 (In Russ).


Full text in PDF

Received 15.08.2022

Revised 02.09.2022

Accepted 15.09.2022

Published 16.09.2022