RT-Techpriemka’s Project for Ensuring Aviation Safety

JSC RT-Techpriemka, a Rostec subsidiary, together with the Aviation Flight Safety service of the Russian Armed Forces will implement a project to introduce advanced machine learning in the production and operation of military aircraft. This was announced by the press service of RT-Techpriemka on Friday.

 

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The use of artificial intelligence allows carrying out long-term planning of the quality of the product and predicting the need for highly accurate equipment repair. The new approach to collecting and analysing data will increase forecast accuracy by about 90%.

The implementation of advanced machine learning aims to minimise safety risks. The method is based on the use of predictive analytics which allows predicting the behavior of technology in the future.

“We are positive about the idea of ​​using modern technologies, including artificial intelligence. The availability of objective and complete information about the root causes of possible failures of aviation equipment at the level of technological processes will help prevent more serious incidents. After all, where the technological process is built, safety is ensured,” said Sergei Bainetov, Head of the Aviation Flight Safety Service of the Russian Armed Forces.

To create an innovative forecasting system, large-scale work will be carried out to collect and digitise production indicators. In particular, the volume of production, the parameters of technological processes, the speed and quality of processing of complaint events will be analyzed. Based on the received data, the "smart" system will study the production and operation processes, calculate their optimal parameters, and will be able to issue recommendations to the operator. Machine learning technology will enable creating a mathematical model of the production process, which can then be integrated into an automated control system.

“The introduction of artificial intelligence will make it possible to solve many issues at all stages of the life cycle of military aircraft, starting from the moment of design. Thanks to new technologies, maintenance work is optimised, downtime will be reduced, and the operator will have complete information about the condition of the equipment and, most importantly, will be able to prevent equipment failure. Now we are on the verge of a large-scale study, on the basis of which the model will be trained. To achieve maximum accuracy, it needs to be trained in several stages, adding or removing some datasets. The final product should be a web service or a mobile application with a user-friendly interface,” said Vladlen Shorin, Director General of JSC RT-Techpriemka.

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