In mid-December 2025, the РА "Red Crescent Society of the Republic of Kazakhstan" held a short film competition on the theme "Fundamental Principles o Read more
Quotation gallery
You can get help from teachers, but you are going to have to learn a lot by yourself, sitting alone in a room.
Doctor Seuss
Faculty News
On December 26, 2025, the Faculty of History, Economics and Law held a final meeting of the Faculty Council dedicated to summarizing the results of th Read more
17.12.2025 на медицинском факультете НАО СКУ им.М.Козыбаева началась экзаменационная сессия. С целью укрепление принципов академической честности, про Read more
12 декабря 2025 года, в рамках реализации плана научных мероприятий медицинского факультета на 2025–2026 годы, был проведён практический семинар с гру Read more
On December 2, 2025, the Department of Justice of the North Kazakhstan region held a lecture for students as part of the implementation of the ideolog Read more
On November 27, 2025, the Faculty of History, Economics and Law hosted a seminar by business consultant and MBA teacher Sh.I. Orazbayeva on the topic Read more
На факультете прошёл круглый стол со студентами 5 курса фармацевтического профиля на тему «Надлежащие аптечные практики: цели, задачи и подходы к реал Read more
From November 12, 2025 at the medical faculty of SKU named after M. Kozybaev as a visiting professor, prof. Claudio Colosio from the University of Mil Read more
19.11.2025 года на медицинском факультете был проведен круглый стол «Патриотизм как научная и нравственная позиция Манаша Козыбаева», посвящённый дню Read more
Круглый стол «Роль Манаша Козыбаева в развитии гуманитарного образования в Казахстане», посвящённый дню рождения выдающегося казахстанского учёного-гу Read more
Development of intelligent computer devices for diagnostics and monitoring of electric power equipment based on identification measurements, methods of deep machine learning Deep Leaning and Big Data science
Priority direction: Energy and mechanical engineering
Project leader: Koshekov K.T., Ph. D.
The project: Ritter D.V., PhD., Kobenko V.Yu., PhD., Buoys, K.A., PhD., Kashevkin A.A., candidate of technical Sciences, PhD student, Kalanchevskaya N.A. master of science, PhD student, Latypov S.I., master of science, PhD student.
Terms of execution: 3 years.
Amount of funding: 62,000,000 tenge.
Project goal: Creation of computer devices and systems for monitoring and diagnostics, including software based on intelligent algorithms for collecting, primary processing and recognition of diagnostic and control signals of electric power equipment using the theory of identification measurements, computer and wireless infocommunication technologies in real time.
Expected result: Expected scientific and socio-economic impact:
- methodology for increasing energy savings through the introduction of intelligent technologies;
- methods and tools for diagnostics and monitoring of electric power equipment based on identification measurements of diagnostic and control signals, Deep Leaning and Big Data science and their reduction of environmental impact;
- improving the quality and speed of diagnostics and monitoring of high-voltage power equipment;
- getting new useful knowledge of energy saving through Deep Leaning in the electric power industry;
- development of information and communication technologies in the electric power industry.
Creation of experimental samples with subsequent testing at leading energy companies.
The target consumers of the results obtained are domestic and foreign enterprises for the production, transmission and distribution of electric energy, as well as organizations that develop equipment.
The use of identification measurements is ideal for solving problems of intelligent diagnostics of complex objects, creating fundamentally new equipment with processing of linguistic characteristics.
The use of Deep machine learning methods Deep Leaning and Big Data technologies will give researchers powerful tools for analyzing power equipment and developing new effective strategies for predicting performance. Obtaining European and Eurasian patents.
Project description: the Project is aimed at improving the efficiency of diagnostics and forecasting of electric power equipment failures by implementing a set of solutions that include Big Data tools and deep machine learning methods for analyzing informative signals (electrical, acoustic, vibration).
The project will result in the creation of intelligent computer devices and hardware and software complex for automated extraction of diagnostic information from informative signals.
Project objective:
−conducting a detailed analysis on the state of the issue in the field of creating intelligent devices and systems in the electric power industry;
−analysis of the possibilities of applying the theory of identification Big Data and deep machine learning measurements. Development of technical solutions for creating devices and equipment diagnostics and monitoring systems;
−development of technical requirements and technical specifications for the development of intelligent computer devices and systems for diagnostics and monitoring of electric power equipment;
−development of algorithms and methods for collecting and intelligent signal processing based on the theory of identification measurements for electric power equipment;
−development of intelligent computer devices and systems for diagnostics and monitoring of electric power equipment;−development of a set of design documentation for experimental samples;
−production of experimental samples of computer devices and diagnostic and monitoring systems;
−testing of experimental samples of devices and systems.