Zamira Amirova, a 2nd-year student of the Department of Journalism and Social Sciences at the Faculty of History, Economics, and Law, achieved outs Read more
Quotation gallery
Knowledge is like money: the more he gets, the more he craves.
Billings
Faculty News
On March 19, 2026, the Faculty of History, Economics, and Law celebrated the grand opening of a new recreation area, the «Kitchen Room». The event too Read more
On March 18, 2026, as part of the Day of National Clothing, a vibrant flash mob titled “National Clothing – Pride of the Nation” was held in the Educa Read more
On March 17, 2026, as part of the celebration of Shanyrak Day, a round table titled “Shanyrak – a Symbol of Unity and Health” was held at the Educatio Read more
On March 16, 2026, as part of the Day of Culture and National Traditions, an interactive platform titled “Traditional Medicine and Culture of the Kaza Read more
On March 15, 2026, as part of Charity Day, a volunteer campaign titled “Do Good — Warm a Heart!” was organized at the Faculty of Medicine. Students o Read more
On March 14, 2026, the Faculty of History, Economics, and Law hosted a festive event dedicated to the traditional Kazakh holiday «Kөrisu kuni», also k Read more
On the eve of the celebration of Gratitude Day, on February 27, 2026, a charity fair was held at the Faculty of History, Economics and Law, bringing t Read more
On February 27, 2026, in anticipation of the «celebration of Gratitude Day», the Faculty of History, Economics, and Law hosted a warm gathering and a Read more
On February 27, 2026, the Faculty of History, Economics and Law held a career guidance meeting with students of the «Foundation» program and future ap 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.