PSI - Issue 20

E.F. Dubinin et al. / Procedia Structural Integrity 20 (2019) 103–107 E.F. Dubinin and V.I. Kuksova / Structural Integrity Procedia 00 (2019) 000 – 000

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b) a dimensionless technical condition index is calculated, which is described by a one-dimensional function, the numerical values of which depend on controlled components of process. Generally, the generalizing parameter of technical condition of OD can be presented as a functional: Y = F ( x i ,x i accept ,x i opt ,a i ), i=1,n , where x i – the current value of the i -th parameter characterizing the state of OD; x i accept – the maximum acceptable value of the i -th parameter; x i opt – the optimal value of the i- th parameter for accident free functioning of OD; a i – the significance (weighting factor) of the i -th parameter; n – the number of parameters included in diagnostic model. When convolving private parameters into generalizing parameter, it is necessary: to define relative values of private parameters; to estimate the significance of private parameter for assessing the state of an object; to construct a mathematical expression for a generalizing parameter. 2.3. Formation of the block of decision making and issue of recommendations (of controlling impacts) In the classic fuzzy inference mechanism, it is assumed that the input variables are of equal value for the conclusion (for the result to be obtained). Actually various signs can have very different impact on obtaining the conclusion. Therefore, in some cases, a weight component reflecting the relative importance of various initial conditions in relation to the same p ossible reason, is entered into the production rule of the type IF «…» AND «…» THEN «…». The technique of accounting for the weights of the initial conditions depends on the specific conditions of application of the fuzzy inference mechanism and requires checking for significance, consistency and efficiency. 3. Conclusions The features of models based on fuzzy logic methods define the area of their effective use in technical diagnostics systems as diagnosing malfunctions, which develop relatively slowly or being detected by diagnostic tools at an early stage. Now there is no uniform standard and methodical base defining standard stages of creation of the models and expert systems using methods of fuzzy logic. Outlined in the report stages of formation of a system of the technical diagnostics based on application of fuzzy methods can be used during the modeling and drawing up the corresponding methodical recommendations. References Akhmetkhanov, R.S., Dubinin, E.F., Kuksova, V.I., 2018. Application of methods and models of fuzzy logic in technical diagnostics systems. Machine Drives and Parts 1-2 (27), 6-11. (in Russian) Akhmetkhanov, R.S., Dubinin, E.F., Kuksova, V.I., 2015. Application of fuzzy sets for risk assessment and management. Problems of Safety in Emergency Situations 4, 56-71. (in Russian) Ibragimov, V.A., 2009. Elements of Fuzzy Mathematics. Ministry of Education of Azerbaijan Republic and Azerbaijan State Oil Academy (Ed.). Baku, pp. 391. (in Russian) Katasev, A.S., 2013. Mathematical and software for the formation of knowledge bases of soft expert systems for diagnosing the state of complex objects: a monograph. State Budgetary Institution «Republican Center for Monitoring the Quality of Education» (Ed.). Kaza n, pp. 200. (in Russian) Mashoshin, O.F., 2007. Aircraft diagnostics. MSTU GA (Ed.). Moscow, pp. 141. (in Russian) Sibikina ,I.V., 2016. Information safety risk analysis using a fuzzy inference system. Scientific Bulletin of NSTU 65 (4), 121-134. (in Russian)