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- Article name
- INTELLIGENT MAINTENANCE DECISION SUPPORT SYSTEM OF CHANNEL OF BARREL SMALL ARMS
- Authors
- Spiryagin V. V., , V.V.Spiryagin@yandex.ru, Moscow Aviation Institute (National Research University), Moscow, Russia
Danyakin N. V., , varhbz@mil.ru, Military Academy of Radiation, Chemical and Biological Protection named after Marshal of the Soviet Union S. K. Timoshenko, Kostroma, Russia
Kozlova M. A., , kmari2016@mail.ru, Military Academy of Radiation, Chemical and Biological Protection named after Marshal of the Soviet Union S. K. Timoshenko, Kostroma, Russia
- Keywords
- barrel bore / small arms / maintenance / defect / corrosion / carbon deposits / chipped chrome / knowledge base / artificial intelligence / expert system / computer vision system
- Year
- 2024 Issue 4 Pages 9 - 17
- Code EDN
- AANIMG
- Code DOI
- 10.52190/1729-6552_2024_4_9
- Abstract
- The article presents a computer vision system based on a neural network for detecting defects in the bore of small arms (SA), which allows automating the process of recognizing and classifying SA defects and reducing the dependence of diagnostic results on the human factor. The article proposes the use of machine learning algorithms and neural network classification in the analysis of SA damage by the visual method of non-destructive testing, which reduces the complexity of work to eliminate defects in the highway and significantly increases their effectiveness. The possibilities of building a decision support system (expert system) are considered according to the choice of the type of maintenance (maintenance) of the SA based on the results of neural network diagnostics. A knowledge base based on the production model has been developed to select the type of maintenance.
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