PSI - Issue 20

Available online at www.sciencedirect.com Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia 00 (2018) 000 – 000 Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia 00 (2018) 000 – 000

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Procedia Structural Integrity 20 (2019) 103–107

1st International Conference on Integrity and Lifetime in Extreme Environment (ILEE-2019) Methods and models of fuzzy logic in the systems of technical diagnostics E.F. Dubinin*, V.I. Kuksova Mechanical Engineering Research Institute, RAS, 4, M. Kharitonyevskiy Pereulok, 101990 Moscow, Russian Federation In the report the main stages of the formation of a system of technical diagnostics based on the use of fuzzy methods are considered: creation of information model of the object of diagnosis (OD); creation on this basis of a fuzzy model describing the behavior of the object; formation of the block of decision-making and issue of recommendations. The field of application of models based on methods of fuzzy logic is diagnosing of the malfunctions developing relatively slowly or which are found out at an early stage. Several variants of formation of rules for fuzzy inference and of the resulting model are possible. When creating a model without generalizing parameter, all possible OD states can be leaded to two types – workable and inoperable, or to several states. For a model with a generalizing parameter, a criterial parameter is selected from private informative parameters; its change should maximally characterize the quality of OD. As a generalizing parameter, one can also calculate the dimensionless technical condition index, which is described by a one-dimensional function, the numerical values of which depend on controlled components of process. Outlined in the report stages of formation of a system of the technical diagnostics can be used during the modeling and drawing up the corresponding methodical recommendations. 1st International Conference on Integrity and Lifetime in Extreme Environment (ILEE-2019) Methods and models of fuzzy logic in the systems of technical diagnostics E.F. Dubinin*, V.I. Kuksova Mechanical Engineering Research Institute, RAS, 4, M. Kharitonyevskiy Pereulok, 101990 Moscow, Russian Federation Abstract In the r port the main stages of the formation f a system of technical diagn stics ba ed on the se of fuzzy methods ar conside ed: creation of information mod l of the obj t of diagnosis (OD); cr ation on this basis of a fuzzy model describing the behavior of the object; formation of the block f decision- aking and issue of recommendations. The field of applicati n of models based on methods of fuzzy logic s diagnosing of the mal unctions eveloping relatively slowly or which are found out at an early stage. S v variants of form tion of rules for fuzzy i fer nce and f the resulting mode are possible. When creating a model without gen ralizing parameter, all ossible OD stat s can be lead d to two types – workabl a d inoperable, or to several states. For a model with a generalizing par meter, a criteri l parameter is selected from private informative parameters; it change shoul maximally characterize the quality f OD. As a generalizing paramet r, one can also calculat the dimension ess technical condition index, which is desc ibed by a one-dimensi al unction, the num rical values of which dep nd on controlled compo ents of process. Outlined in the report s ages of formation of a sy tem of the technical diagnostics can be used during the modeling and drawing up the corresponding methodical recommendations. Abstract

© 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the ILEE-2019 organizers © 2019 The Author(s). Published by Elsevie r B.V. Peer-review under responsibility of the ILEE-2019 organizers © 2019 The Autho ( ). Published by Elsevie r B.V. Peer-review under responsibility of the ILEE-2019 organizers

Keywords: System of technical diagnostics; fuzzy logic methods; fuzzy logic models; generalizing parameter Keywords: System of technical diagnostics; fuzzy logic methods; fuzzy logic models; generalizing parameter

* Corresponding author. Tel.: +7-916-529-0962; E-mail address: mibsts@mail.ru * Correspon ing author. Tel.: +7-916-529-0962; E-mail address: mibsts@mail.ru

2452-3216 © 2019 The Author(s). Published by Elsevier B.V. Peer-review under responsibility of the ILEE-2019 organizers 2452 3216 © 2019 Th Author(s). Publis d by lsevier B.V. Peer-review under responsibility of the ILEE-2019 organizers

2452-3216 © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the ILEE-2019 organizers 10.1016/j.prostr.2019.12.123