The article presents the main approaches to evaluating the reliability of newly designed technical devices using virtual and full-scale tests. As a rule, when starting the development of new complicated objects, a small number of objects are tested and in this case there is no sufficiently substantiated possibility of static evaluation of the reliability results using a classical approach, when the selected theoretical distribution is checked for compliance with experimental data, distribution parameters are determined, etc. The article proposes to conduct an evaluation of the reliability of the designed technical systems in two stages. The first stage involves conducting virtual tests of 3D models of a pilot sample using numerical methods and techniques for constructing mathematical models using ANSYS software as a part of the CFD computational fluid dynamics and Mechanical Enterprise strength calculation packages (including the Logos software product). The second stage is the determination of the probability of failure-free operation based on a small number of tests without determining the distribution function, using the nonparametric statistical Mann criterion; and it also provides the possibility, with a small number of failures, of using an estimated probability of failure-free operation, taking into account the accumulation of information. To establish the causes of failures of pilot samples, a set of measures is proposed to establish and prevent the causes of failure and ensure the stable operation of the product.
References
1. Frolov K.V., Metody sovershenstvovaniya mashin i sovremennye problemy mashinostroeniya (Methods of improving machines and modern problems of mechanical engineering), Moscow: Mashinostroenie Publ., 1984, 224 p.
2. Viktorova V.S., Agregirovanie modeley analiza nadezhnosti i bezopasnosti tekhnicheskikh sistem slozhnoy struktury (Aggregation of models for the analysis of reliability and safety of technical systems of complex structure): thesis of doctor of technical science, Moscow, 2009.
3. Gorbunova E.B., Metod statisticheskoy obrabotki malykh vyborok dannykh v zadachakh prognozirovaniya i kontrolya sostoyaniya slozhnykh sistem (Method of statistical processing of small data samples in problems of forecasting and monitoring the state of complex systems): thesis of candidate of technical science, 2018.
4. Voynov K.N., Prognozirovanie nadezhnosti mekhanicheskikh sistem (Predicting the reliability of mechanical systems), Leningrad: Mashinostroenie Publ., 1978, 208 p.
5. Burumkulov F.Kh., Lezin P.P., Rabotosposobnost’ i dolgovechnost’ vosstanavlivaemykh detaley i sborochnykh edinits mashin (Performance and durability of restored parts and assembly units of machines), Saransk: Publ. of Mordovian University, 1993, 119 p.
6. Gaskarov D.V., Shapovalov V.I., Malaya vyborka (Small sampling), Moscow: Statistika Publ., 1978, 248 p.
7. Zarenin Yu.G, Stoyanova I.I., Opredelenie ispytaniy na nadezhnost’ (Definition of reliability testing), Moscow: Publishing house of standards, 1978, 168 p.
8. Chavchanidze V.V., Kumsishvili V.A., Primenenie vychislitel’noy tekhniki dlya avtomatizatsii proizvodstva (Application of computer technology for automation of production), Moscow: Mashgiz Publ., 2001.
9. Hastings N.A.J., Peacock J.B., Statistical distributions: A handbook for students and practitioners, Wiley, 1975, 130 p.
10. Aralov O.V., Buyanov I.V., Analysis of methods and approaches to reliability assessment in the prediction of main pipeline transport equipment failures (In Russ.), Nauka i tehnologii truboprovodnogo transporta nefti i nefteproduktov = Science & Technologies: Oil and Oil Products Pipeline Transportation, 2017, V. 7, no. 6, pp. 104–114.
11. Bogomolov M.V., Virtual testing of products (In Russ.), Trudy MAI, 2010, V. 38, URL: https://trudymai.ru/upload/iblock/552/virtualnye-ispytaniya-izdeliy.pdf
12. Tkachenko A.Yu., Rybakov V.N., Krupenich I.N. et al., Computer-aided system of virtual gas turbine engine testing (In Russ.), Vestnik SGAU = VESTNIK of Samara University Aerospace and Mechanical Engineering, 2014, no. 5-3 (47), DOI: http://doi.org/10.18287/1998-6629-2014-0-5-3(47)-113-119
13. Abdullaev M.U., Kishkin A.A., Technologies of numerical and virtual experiments in engineering (In Russ.), Aktual’nye problemy aviatsii i kosmonavtiki, 2022, V. 1,
pp. 240-241.
14. Golovkova Yu.S., Numerical modeling in ANSYS program (In Russ.), Problemy nauki, 2020, no. 6 (54), pp. 43-44.
15. Garipov A.A., Konstantinov S.Yu., Tuk D.E., Tselishchev D.V., Computational fluid flow modeling in filter (In Russ.), Vestnik UGATU, 2013, no. 3(56), pp. 153-158.
16. Buyanov I.V., The main approaches to predicting the reliability of machines during development (In Russ.), Nauka i tehnologii truboprovodnogo transporta nefti i nefteproduktov = Science & Technologies: Oil and Oil Products Pipeline Transportation, 2024, V. 14, no. 5, pp. 440–449, DOI: https://doi.org/10.28999/2541-9595-2024-14-5-440-449
17. Lloyd D.K., Lipow M., Reliability: Management, methods, and mathematics, Prentice Hall, 1962, 528 p.
18. Barlow R.E., Proschan F., Mathematical theory of reliability, John Wiley and Sons, 1965, 261 p.
19. Pronikov A.S., Parametricheskaya nadezhnost’ mashin (Parametric reliability of machines), Moscow: Publ. of Moscow State Technical University N.E. Bauman,
2002, 560 p.
20. Lipkin I.A., Statisticheskaya radiotekhnika. Teoriya informatsii i kodirovaniya (Statistical radio engineering. Information and coding theory), Moscow: Vuzovskaya kniga Publ., 2002, 216 p.
