Abstract
The problem of the limited hardware capability of the parametric tolerance control process of the state of technical systems is considered. A more complete assessment of the technical condition of a workable product is necessary to support decision making and reduce risks. An approach to estimating the parameters of systems based on the theory of fuzzy sets to determine the state characterized by considerable uncertainty and incompleteness of information for its modeling by traditional methods is proposed. This approach is applicable to the organization of tolerance control at different stages of the life cycle. This approach uses an additional fuzzy classification of parameter values to increase the reliability of control results, taking into account uncertainty factors. It is proposed to use the working capacity criterion, the criterion for the steadiness of the tendency of the dynamics, the criterion of the rate of change of the parameter, and the complex criterion for working capacity level in addition to the criterion of belonging to tolerance zones. Four fuzzy classifiers have been developed, which allow to take into account the inaccuracy and approximation of the initial information, operate with linguistic criteria and include qualitative variables in the analysis. The procedure for estimating the value of the parameter according to the complex criterion for working capacity level is considered.
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References
G.I. Korshunov, S.A. Nazarevich, V.A. Smirnov, Fuzzy classification of technical condition at life cycle stages of responsible appointment systems, in Proceedings of the II International Scientific and Practical Conference “Fuzzy Technologies in the Industry—FTI 2018”, Ulyanovsk, Russia, 23–25 Oct 2018, vol. 2258. CEUR Workshop Proceedings, pp. 427–437
V.A. Smirnov, Malfunction searching in onboard control systems during acceptance control. Informatsionno-upravlyayushchie sistemy [Inf. Manage. Syst.] 2, 24–28 (2013) (in Russian)
L.A. Zadeh, Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst. 1, 3–28 (1978)
S. Nahmias, Fuzzy variables. Fuzzy Sets Syst. 1, 97–110 (1978)
K.M. Passino, S. Yurkovich, Fuzzy Control (Addison Wesley Longman, Boston, USA, 1998), p. 522
M. Friedman, M. Ming, A. Kandel, Fuzzy linear systems. Fuzzy Sets Syst. 96, 201–209 (1998)
R. Brachman, P. Sefridge, Knowledge representation support for data archeology. Intell. Cooper. Inform. Syst. 2, 113–120 (1993)
R.E. Bellman, L.A. Zadeh, Decision making in a fuzzy environment. Manage. Sci. 17, 141–164 (1970)
A.V. Leonenkov, Nechetkoe modelirovanie v srede MATLAB i fuzzy TECH [Fuzzy Modeling in MATLAB and fuzzyTECH] (BKHV-Petersburg, St. Petersburg, 2005) 736p (in Russian)
A.O. Nedosekin, Nechetko-mnozhestvennyj analiz riska fondovyh investicij [Fuzzy Multiple Risk Analysis of Stock Investment] (Printing House “Sesame”, St. Petersburg, 2002) 181p
P.C. Fishburn, Utility Theory for Decision Making (Wiley, New York, 1970), p. 234
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Korshunov, G., Smirnov, V., Frolova, E., Nazarevich, S. (2020). Fuzzy Models and System Technical Condition Estimation Criteria. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Fourth International Congress on Information and Communication Technology. Advances in Intelligent Systems and Computing, vol 1041. Springer, Singapore. https://doi.org/10.1007/978-981-15-0637-6_15
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DOI: https://doi.org/10.1007/978-981-15-0637-6_15
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