UDC 378::629.7-057.4
DOI 10.20339/AM.07-26.120
Vladimir T. Kalugin, D.Sc. (Engineering), Professor, Head of the NUC SM Bauman Moscow State Technical University
Alexander Yu. Lutsenko, Cand. Sci. (Engineering), Docent, First Deputy Dean of the Faculty of SM Bauman Moscow State Technical University
Irina K. Romanova-Bolshakova, D.Sc. (Engineering), Docent, Deputy Dean for Master’s Degree of the Faculty of SM Bauman Moscow State Technical University, e-mail: irina.romanova@bmstu.ru, https://orcid.org/0000-0002-5757-350X
The article analyzes the features of training rocket and space technology specialists in the field of predictive maintenance, diagnostics and risks of complex systems based on an integrated interdisciplinary approach aimed at developing competencies related to different life stages of aviation and rocket and space technology products. Relevant competencies for a modern specialist are highlighted, such as assessing and predicting the technical condition of units and systems of launch complexes, ground-based debugging systems, communication systems with on-board computing facilities, real-time modeling systems, and other technical facilities, identifying possible malfunctions, analyzing the causes of their occurrence and making decisions to eliminate them, and mathematical modeling of operation launch complex equipment, justification of the scope and time of routine maintenance and repair work, analysis of the risks of operating potentially dangerous objects of rocket and space technology, development of measures for their management, use of methods of non-destructive testing of the technical condition of space technology. The experience of training specialists in this field at the Bauman Moscow State Technical University in the field of 24 UCSN is analyzed and measures are proposed to improve an integrated interdisciplinary approach.
Keywords: rocket and space technology, reliability of RCT products and their main components, measures to ensure reliability, diagnostics, predictive maintenance, risks, machine learning, training of RCT engineering personnel, integrated competencies
References
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