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Intelligent diagnostics of cleanroom ventilation and air conditioning systems
https://doi.org/10.17586/0021-3454-2025-68-2-168-175
Abstract
An approach to training diagnostic models of complex technical systems with multiple uncertainty of a priori information is proposed. Since it is impossible to determine the law of distribution of values of parameters of working processes, it is proposed to use methods of nonparametric statistics. The training procedure is based on the use of topology and properties of finite-dimensional Euclidean spaces. An example of a training procedure using a computational scheme according to the Robbins-Monroe algorithm is given. A graphical interpretation of the construction of a standard of parametric failure of an element when constructing diagnostic models of equipment of the ventilation and air conditioning system of a clean room of a special facility is presented.
About the Authors
Yu. E. Tupitsin
A. F. Mozhaisky Military Space Academy
Russian Federation
Russian Federation
Yuri E. Tupitsin - PhD, Associate Professor; Department o Life Support Systems for Ground-Based Space Infrastructure Facilities
St. Petersburg
A. S. Matyunin
A. F. Mozhaisky Military Space Academy
Russian Federation
Russian Federation
Alexander S. Matyunin - PhD; Department of Life Support Systems for Ground-Based Space Infrastructure Facilities; Lecturer
St. Petersburg
M. V. Egorichev
A. F. Mozhaisky Military Space Academy
Russian Federation
Russian Federation
Maxim V. Egorichev - Adjunct; Department of Life Suppor Systems for Ground-Based Space Infrastructure Facilities; Lecturer
St. Petersburg
A. A. Golub
A. F. Mozhaisky Military Space Academy
Russian Federation
Russian Federation
Andrey A. Golub - Cadet; Department of Life Support Systems for Ground-Based Space Infrastructure Facilities
St. Petersburg
References
1. Fomin Ya. A. Raspoznavaniye obrazov. Teoriya i primeneniya (Pattern Recognition. Theory and Applications), Moscow, 2010, 368 р. (in Russ.)
2. Loban A.V. Informatsionnaya tekhnologiya raspredelennogo diagnostirovaniya kosmicheskikh apparatov (Information Technology of Distributed Diagnostics of Spacecraft), Moscow, Berlin, 2015, 146 р. (in Russ.)
3. Senchenkov V.I. Modeli, metody i algoritmy analiza tekhnicheskogo sostoyaniya (Models, Methods and Algorithms for Technical Condition Analysis), Saarbrücken, 2013, 377 р. (in Russ.)
4. Chunhui Z., Furong G. Chemical Engineering Science, 2015, vol. 138, рр. 531–543.
5. Lu G., Zhou Y., Lu C., Li X. Mechanical Systems and Signal Processing, 2017, vol. 83, рр. 533–548.
6. Budko P.A., Vinogradenko A.M., Litvinov A.I. Mechatronics, automation, control, 2014, no. 9, pp. 53–58. (in Russ.)
7. Liu W.Y., Gao Q.W., Ye G. et al. Measurement, 2015, vol. 74, рр. 70–77.
8. Skliros C., Esperon M.M., Fakhre A., Jennions I.K. Diagnostyka, 2019, vol. 20(1), рр. 3–21.
9. Shi P., Liang K., Han D., Zhang Yi. Journal of Vibroengineering, 2017, vol. 19(8), рр. 5932–5946.
10. Senchenkov V.I., Matyunin A.S. Pribory i sistemy. Upravleniye, kontrol’, diagnostika, 2020, no. 8, pp. 18–26. (in Russ.)
Review
For citations:
Tupitsin Yu.E., Matyunin A.S., Egorichev M.V., Golub A.A. Intelligent diagnostics of cleanroom ventilation and air conditioning systems. Journal of Instrument Engineering. 2025;68(2):168-175. (In Russ.) https://doi.org/10.17586/0021-3454-2025-68-2-168-175
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