=Paper=
{{Paper
|id=Vol-2258/paper51
|storemode=property
|title=Fuzzy Classification of Technical Condition at Life Cycle Stages of Responsible Appointment Systems
|pdfUrl=https://ceur-ws.org/Vol-2258/paper51.pdf
|volume=Vol-2258
|authors=Gennady Korshunov,Stanislav Nazarevich,Vladimir Smirnov
}}
==Fuzzy Classification of Technical Condition at Life Cycle Stages of Responsible Appointment Systems==
https://ceur-ws.org/Vol-2258/paper51.pdf
Fuzzy classification of technical condition at life cycle stages of
responsible appointment systems
G I Korshunov1, 2, S A Nazarevich1 and V A Smirnov3
1
State Autonomous Educational Institution of Higher Education "Saint-Petersburg State
University of Aerospace Instrumentation" (SUAI), ul. BolshayaMorskaya, 67, lit. A,
St. Petersburg, RussianFederation, 190000
2
Ciberphisic systems and control high school, Peter the Great St. Petersburg Polytechnic
University (SPB STU), 29, Polytechnicheskaya St., Saint-Peterburg, Russian Federation,
195251
3
Сlosed joint stock company "Scientific-production center "Akvamarin", Tallinskaya St., 7,
St. Petersburg, RussianFederation, 195196
Abstract. Approaches are considered and models of fuzzy classification of the technical state
of systems of responsible design are proposed. For such systems, known performance
estimates in the tolerance field are supplemented by the evaluation of the proximity of the
parameter to the tolerance boundary and the anomaly state. The offered approach on three
criteria is offered to extend on all stages of the life cycle connected with the control of a
technical condition. For innovative products, a fuzzy model for estimating the drift of
parameters near the primary base model is proposed. The structure of an intellectual decision
support system designed to uncover uncertainties in the technical state on the basis of the
updated knowledge base is considered.
1. Introduction
The tasks of identification and assessment of technical condition are topical for all technical systems.
Failure of on-board automated systems, automation systems for energy, transport and other objects of
responsible use leads to catastrophes, significant damage or other losses. The assessment of the
technical condition of such responsible appointment systems(RAS) raises increased requirements for
uncovering the uncertainty associated with their technical condition. In the article, in addition to the
known probabilistic characteristics associated with parametric tolerance control, it is proposed to
introduce fuzzy estimates of the technical state at the stages of the life cycle. When creating an
innovative SES of the estimation of fuzzy sets characterizing the uncertainty of the type "located in the
interval" corresponding to the tolerated parametric control with the lower and upper tolerances,
piecewise linear functions and bell-shaped functions are used.
Theobtainedestimatescharacterizethedriftoftheparametersneartheprimarybasemodel (Figure 1). For the
stages of production and operation of RAS, a model of complex assessments "the level of operability"
of parameters by the criteria of closeness of the values of the parameters to the boundaries of tolerance
fields and the anomalous behavior of the values of the parameters within the limits of tolerance fields
is proposed. Models for estimating fuzzy categories are proposed to be integrated into the intellectual
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decision support system designed to uncover technical uncertainties based on the updated knowledge
base.
2.Fuzzy assessment of the innovative products parameters
The current practice of automated parametric control involves traditional methods of assessing the
performance of the product, based on the criteria for the belonging of the values of the parameters to
the corresponding tolerance zones. The binary approach does not take into account the boundary
conditions of the investigated object in the control of RAS, which are necessary for forecasting
potential supernumerary situations. Parametric control and maintenance of the target state of RAS is a
complex task with a multitude of states and possible outcomes. The problem of determining the
trajectory of the development of basic structures, to which the investigated RAS belongs, is reduced to
the localization of the base sample, as a set of functionally necessary and functionally sufficient
parameters [1]. The values of the parameters can vary under the influence of various scientific and
industrial and social factors. The reaction to the impact is expressed in the form of creating technical
and consumer characteristics and changing (drift) parameters (figure 1). Expansion of the scope of
application of functionally sufficient parameters, creates the possibility of product diversification for
the market.
Updated restrictions on social standards (S3)
Y3
Z2" (Y2" ;Х2") Z3 (Y3 ;Х3)
Limitations of social standards (S2)
Y2
Consumer characteristics
Z2 (Y2;Х2)
Basic structure
Z2' (Y2';Х3) Limitations of the Prior Art (M2)
Z1 (Y1;Х1)
Y1
Z1' (Y1';Х2)
Z0 (Y0;Х0)
Y0
Z0' (Y0';Х1)
Х0 Х1 Х2 Х3
Specifications
Target state Projected state
Figure 1. Drift of parameters.
Reaching the values of functionally necessary parameters allows you to stay in the local extremum
of the product parameters to determine the technical novelty of the product or the degree of novelty of
the product. However, it is difficult to say how the product behaves when performing the objective
function under the load of external influencing factors (EIF). Therefore, preliminary tests are designed
to identify significant EIF that affect the state of RAS. Features of the design of the basic structures of
RAS include an assessment of the achievement of functionally significant (critical) parameters.
The use of auxiliary tools presented in the work of Deming [2], in the process of decision-making on
the basis of facts, the selection of functionally significant parameters of the RAS is reduced to the
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search and acceptance for the basis of the existing base sample, from which the developers of the new
RAS will be repelled.
The ranking, synthesis and choice of options for the development of new RAS is carried out in the
morphological space of parameters obtained by monitoring the reference points in the process of
operation. Thus, several potential objects are formed in some neighborhood of the base point. The
concept of the base point is based on the notion of a basic model created to solve current problems in
the industry. The base sample is the basis for the generally accepted scientific and industrial paradigm
existing at the time of its creation. To improve the search process in the space of parameters of a
rational structure, within the framework of planned technological development by methods (KAIRYO)
and (KAIZEN), it is also advisable to take into account the laws of the development of technical
systems proposed by the theory of solutions of inventive problems (TRIZ).
Bs Mod Mod Mod
1,0
Inn (+)
Inn (+)
Inn (+)
Deg (-)
Deg (-)
Deg (-)
0,5
KAIZEN KAIZEN KAIZEN
KAIRYO KAIRYO KAIRYO
0,0
Bs Bs
Figure 2. Themembership functions for investigating the drift of the product parameters. BS - area of
the basic structure; Mod - zone of modernization; KAIRYO - a zone of technological breakthrough;
KAIZEN - zone of continuous improvement; Inn (+) - an area of innovative leap; Deg (-) - zone of
moral (artificial) obsolescence.
Different states of the basic structures created under the influence of the EIF reflect the drift of the
parameters of the RAS. In cases where the result of parameter control is determined and a point state
is required or the result is expressed by a range of values, the membership functions characteristic for
solving such problems are used (figure 2).The use of membership functions depends on the expert's
opinion and is not formalized. In [3] recommendations are given on the areas of application of
membership functions. Following [3], piecewise linear functions are used to specify fuzzy sets
characterizing the uncertainty of the type "located in the interval" corresponding to tolerant parametric
control with lower and upper tolerances. Z-shaped or S-shaped membership functions are used to
specify fuzzy sets characterizing the uncertainty of the "lower tolerance" or "upper tolerance" type,
respectively. It is possible to build bell-shaped functions. When there is a lack of information, it is
recommended to use piecewise linear membership functions that can be corrected by an expert or in
the process of functioning of an intellectual decision support system (IDSS).
3. Fuzzy assessment of technical condition during production and operation
Admission control of parameters is carried out with the help of an automated control system (ACS) in
the process of adjustment, technological run, bearer, acceptance and periodic testing, during the
products operation. The current practice of automated parametric control involves traditional methods
of assessing the performance of the product, based on the criteria for the belonging of the values of the
parameters to the corresponding tolerance zones [4].The data on the control of the values of the
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parameters and the results of the checks come from the ACS on the universal serial bus to the
industrial computer that implements the functions of the IDSS.
New results on the creation of the necessary means for automated control systems for mass-
produced objectsare presented in [5]. Such systems can be used in preliminary and acceptance tests, as
well as during adjustment and adjustment in the production process. Creation of mass-capable objects
with specified quality indicators should be provided at all stages of the product life cycle. The formal
requirements for the reliability of control that can be made can differ at different stages. The concept
of reliability used can be interpreted, depending on the quality requirements, as completeness of
control, depth of control (diagnosis), "manufacturer's risk" and "customer risk". The customer in the
delivery of products is usually interested in the control of the product as a whole. The developer and
the manufacturer, in addition to monitoring the operation, are also interested in the results of
diagnosing for debugging design decisions and manufacturing techniques in order to reduce rejection.
In this setting, the creation of an ACS for the verification of any electronic product is reduced to the
creation of an input driver, a device that reads responses to input influences and decides device,
providing a decision on the operability of the product.
The solution to the problem of increasing the efficiency of ACS operation is associated with an
increase in the accuracy of estimates of the results of parametric control, the quality of recognition,
and the assessment of the technical state of RAS. For the timely detection of pre-existing conditions of
RAS and the detection of predispositions to the instability of an operational state in the early stages of
defect development, improvement of scientific principles and technical controls is required. A
promising way to solve these problems is the development of new models, criteria and algorithms for
a deeper evaluation of the technical state of the product, the decisive rules for recognizing the different
states of a workable system based on the theory of fuzzy sets [6] and fuzzy logic [7].
To increase the accuracy of estimates of the results of parametric control, three fuzzy classifiers of
estimates of the monitored parameters have been developed, which are used in conjunction with the
mathematical apparatus of fuzzy logic in the model to obtain complex estimates of the "working
capacity" parameters.
As parameters to be more accurately estimated, it is proposed to select the critical parameters of the
RAS that characterize the performance of critical elements that have a "fit" rating based on the results
of ACS's tolerance control. The initial data for a more accurate estimation of critical parameters are
their numerical values. The list of critical parameters is determined in accordance with the guidelines
given in [8] and [9].
The construction of the classifier is based on the results of processing expert opinions and
analyzing the existing database of the results of previous tests. The choice of the required number of
values of linguistic variables is made taking into account the need to ensure a minimum degree of
difficulty when using classifiers in the control process and the maximum consistency of expert
judgments in their creation.
The input linguistic variables of the model for obtaining complex estimates of the parameters
"health level" are: A1 - "estimation of the state of the parameter by the criterion C1" and A2 -
"estimation of the state of the parameter by the criterion C2". As A1, we will use the qualitative
estimation of the state of the parameter "fit" by the criterion of proximity to the boundaries of the
tolerance fields, and as A2 - the qualitative evaluation of the state of the parameter "fit" by the criterion
of anomalous behavior of the values within the boundaries of the tolerance fields. " The output
linguistic variable is B - "estimation of the state of the parameter by the criterion C3". The value of B is
an estimate of the state of the parameter "fit" according to the complex criterion "level of efficiency."
The aggregate of linguistic values of A1, A2 and B are presented in table 1.
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Table 1. Setting of term sets for variablesА1, А2иВ.
Designation Linguistic variable Term-set
EC1 – «excellent сondition»,
GC1 – «good condition»,
A1 Evaluation of the parameter
SC1 – «satisfactory condition»,
by the C1 criterion
DC1 – «Dangerous condition»,
PS1 – «Pre-failure status».
EC2 – «excellent сondition»,
Evaluation of the parameter GC2 – «good condition»,
A2 by the C2 criterion SC2 – «satisfactory condition»,
DC2 – «Dangerous condition»,
PS2 – «Pre-failure status».
EC3 – «excellent сondition»,
GC3 – «good condition»,
Evaluation of the parameter
B SC3 – «satisfactory condition»,
by the C3 criterion
DC3 – «Dangerous condition»,
PS3 – «Pre-failure status».
To form the basis of rules for fuzzy inference systems, you must first determine the input and
output linguistic variables. Since different critical parameters can have different values of tolerances,
then as a carrier of linguistic variables А1, А2 and Вthe unit interval of the real axis [0,1] is chosen.
Since finite-dimensional segments of the real axis can be reduced to a segment [0,1] by a simple linear
transformation (dividing all values by the maximum value), then the selected segment of unit length
(relative scale) has a universal character. To describe subsets of the term set (Table 1), we introduce a
system of five membership functions, which are based on triangular membership functions. The
choice of the form of the triangular membership function is justified by the following considerations:
• When estimating the parameters of membership functions, only interval limits and the most
acceptable parameter values are known. If the researcher does not have more information, then the
only acceptable approximation is linear. The triangular membership function is specified by the
minimum number of parameters: the minimum value, the modal value, and the maximum value;
• triangular membership functions have low computational complexity, which makes it
possible to apply them in situations with a limited time limit for making managerial decisions. In
addition, they are widely used in existing applications of fuzzy logic, their reliability and
effectiveness are tested by practice.
The numerical value of the input variable x1 from the interval [0,1] is the distance from the middle
of the tolerance field to its edge, the value of x2 is the rate of change of the parameter, which is the
ratio of the difference between the maximum and minimum values of the parameter from all values
obtained during various tests the difference in the fixation time of these parameters. The value of y is a
numerical estimate of the state of the parameter "fit" according to the complex criterion "level of
efficiency".
Thus, the linguistic variable А1 (А2, В), defined on the unit segment of the real axis [0,1], with a term-
set of values that are described by triangular membership functions, is a five-level fuzzy classifier of
the parameterx1 (x2, y).Graphically, the set of membership functions of the term set of values of the
linguistic variable B is shown in the figure 3.
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PS3 DC3 SC3 GC3 EC3
1
0 0.1 0.3 0.5 0.7 0.9 1 y
Figure 3.Five-level fuzzy classifier for the parameter y.
As an algorithm of fuzzy logic inference, the Mamdani algorithm was chosen, which makes it
possible to significantly reduce the amount of computation that has received the greatest practical
application and was appreciated by specialists [10].Let's consider an example of an estimation of a
condition of a critical parameter "is good" under the complex criterion "level of working capacity" at a
stage of acceptance control at the manufacturing enterprise.Given the above abbreviations and all
combinations, the base of fuzzy rules consists of 25 rules:
RULE 1: IF "A1 is EC1" AND "A2 is EC2", then "B is EC3";
RULE 2: IF "A1 is EC1» AND «A2 is GC2», then "B is GC3»;
RULE 3: IF "A1 is EC1» AND «A2 is SC2», then "B is SC3»;
RULE 4: IF "A1 is EC1» AND«A2 is DC2», then "B is DC3»;
RULE 5: IF "A1 is EC1» AND «A2 is PS2», then "B is PS3»;
RULE 6: IF "A1 is GC1» AND «A2 is EC2», then "B is GC3»;
RULE 7: IF "A1 is GC1» AND «A2 is GC2», then "B is GC3»;
RULE 8: IF "A1 is GC1» AND «A2 is SC2», then "B is SC3»;
RULE 9: IF "A1 is GC1» AND «A2 is DC2», then "B is DC3»;
RULE 10: IF "A1 is GC1» AND«A2 is PS2», then "B is PS3»;
RULE 11: IF «A1 is SC1» AND «A2 is EC2», then "B is SC3»;
RULE 12: IF «A1 is SC1» AND «A2 is GC2», then "B is SC3»;
RULE 13: IF «A1 is SC1» AND «A2 is SC2», then "B is SC3»;
RULE 14: IF «A1 is SC1» AND «A2 is DC2», then "BisDC3»;
RULE 15: IF «A1 is SC1» AND «A2 is PS2», then "BisPS3»;
RULE 16: IF «A1 is DC1» AND «A2 is EC2», then "B is DC3»;
RULE 17: IF «A1 is DC1» AND «A2 is GC2», then "B is DC3»;
RULE 18: IF «A1 is DC1» AND «A2 is SC2», then "B is DC3»;
RULE 19: IF «A1 is DC1» AND «A2 is DC2», then "B is DC3»;
RULE 20: IF «A1 is DC1» AND «A2 is PS2», then "BisPS3»;
RULE 21: IF «A1 is PS1» AND «A2 is EC2», then "B is PS3»;
RULE 22: IF «A1 is PS1» AND «A2 is GC2», then "B is PS3»;
RULE 23: IF «A1 is PS1» AND «A2 is SC2», then "B is PS3»;
RULE 24: IF «A1 is PS1» AND «A2 is DC2», then "B is PS3»;
RULE 25: IF «A1 is PS1» AND «A2 is PS2», then "B is PS3».
Suppose, before the beginning of fuzzification, the numerical values from the interval [0,1] of all
input variables of the fuzzy inference system are known, namely,x1= 0.15 иx2=0.82. The active rules
that will be used in the current process of fuzzy inference are:
RULE 4: IF«A1 is EC1» AND «A2 is DC2», then «B is DC3»;
RULE 5: IF«A1 is EC1» AND «A2 is PS2», then «B is PS3»;
RULE 9: IF«A1 is GC1» AND «A2 is DC2», then «B is DC3»;
RULE 10: IF«A1 is GC1» AND «A2 is PS2», then «B is PS3».
Further, using the membership functions of all terms of the input linguistic variables A1and A2, we
determine the degree of truth of elementary fuzzy sentences, which are the subwords of active fuzzy
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rules 4, 5, 9 and 10.For example, for the term EC1, the membership function formula has the following
form:
1, x 0.1
0.3 - x
μEC1 (x;0.1, 0.3)= , 0.1 x 0.3 , (1)
0.2
0, x 0.3
Таким образом, при фаззификации получаем степени истинности элементарных нечетких
высказываний:
- «A1is an" excellent condition ", μEC1 ( x1 ) = 0.75 ;
- «А1is a" good condition ", μGC1 ( x1 ) = 0.25 ;
- «А2is a" dangerous state ", μDC2 ( x2 ) = 0.4 ;
- «А2 is a" pre-failure state", μPS2 ( x2 ) = 0.6 .
Figures 4 and 5 show examples of the fuzzification of input linguistic variables: "estimation of the
state of the parameter by the criterion K1" and "estimation of the state of the parameter by the criterion
K2". The degree of truth of the fuzzy statement is the ordinate of the point of intersection of the graph
of the membership of the term and the line x equal to the numerical value of the input variable.
EC1 GC1 SC1 DC1 PS1
1
0.75
0.25
0 0.1 0.15 0.3 0.5 0.7 0.9 1 x1
Figure 4. Example of fuzzification of the input linguistic variable "estimation of the state of the
parameter by K1".
EC2 GC2 SC2 DC2 PS2
1
0.6
0.4
0 0.3 0.5 0.7 x2
0.1 0,82 0.9 1
Figure 5. Example of fuzzification of the input linguistic variable "estimation of the state of the
parameter by K2".
At the stage of aggregation of subwords in fuzzy rules (Figure 6), we determine the levels of
"clipping" α (the degree of truth conditions) for each of the active fuzzy rules using the min-activation
method:
α4 = min μEС ( x1 ), μDС ( x2 ) = min(0.75,0.4) = 0.4 ;
1 2
α5 = min μEС1 ( x1 ), μPS2 ( x2 ) = min(0.75,0.6) = 0.6 ;
α9 = min μGС1 ( x1 ), μDС2 ( x2 ) = min(0.25,0.4) = 0.25 ;
α10 = min μGС1 ( x1 ), μPS2 ( x2 ) = min(0.25,0.6) = 0.25 .
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At the stage of activation of conclusions in fuzzy rules (Fig. 4) we find activated ("truncated")
3 ( y ) min 4 , DC ( y ) ;
membership functions using min-activation: DC
4
3
5
PS 3 ( y ) min 5 , PS3 ( y ) ;
3 ( y ) min 9 , DC ( y ) ;
DC
9
3
10
PS 3 ( y ) min 10 , PS3 ( y ) .
At the stage of accumulation of the conclusions of fuzzy rules (Fig. 4) we find the function of μACC
(y) belonging to the output linguistic variable B by combining fuzzy sets
DC
4
3
( y), PS
5
3
( y), DC
9
3
( y), 10
PS3 ( y)
with membership functions by the max-association method:
ACC ( y ) ACC ( y ) max min EC ( x1 ), DC ( x2 ) , min EC ( x1 ), PS ( x2 ) , min EC ( x1 ), DC ( x2 ) ,
1 2 1 2 1 2
min EC1 ( x1 ), PS2 ( x2 )
EC1 DC2 DC3
1 1 1
0.75
0.4 4
0 x1 0.82 x2 0
EC1 0.15 PS2 PS3 0.5 y
1 1 1
0.75
0.6 5
0 0.15 x1 0.82 1 x2 0 0.5 y
1 GC1 DC2 DC3
1 1
0.4
0.25
9
0.15 x1 0.82 x2 0
0.5 y
GC1 PS2 PS3
1 1 1
0.6
0.25
10
0.15 x1 0.82 1 x2 0
0.5 y
1
0 y=0 0.5 y
Figure 6. Example of aggregation of subwords, activation and accumulation of the conclusion and
defuzzification of the output linguistic variable by the method of the right maximum.
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To determine the quantitative value of the output linguistic variable at the stage of defuzzification,
we use the method of the left maximum, which consists in choosing the smallest value y that has the
highest degree of affinity for the fuzzy set. As a result of the reduction to clarity, a numerical estimate
of the state of the parameter "fit" is obtained from the complex criterion "level of efficiency" y = 0.
The following critical parameters are then evaluated. Having received a set of estimates, the decision-
maker selects a possible strategy for further action based on the results of the parametric control,
depending on the level of risk of failure in table 2.
Table 2. Possible strategies for decision-making depending on the level of risk of failure.
The level of risk of failure The criterion of the level
Possible strategies for decision-making
of risk of failure
The decision on acceptance of the product
Acceptable
y Y : 0.7 y 0.9 with a regular complete set of spare parts,
tools and accessories is made
The decision on acceptance of the product
Border y Y : 0.35 y 0.7 with the changed complete set of spare
parts, tools and accessories is made
The decision on acceptance of the product is
not accepted. A program for the
Unacceptable y Y : 0 y 0.35 implementation of preventive measures and
subsequent decision-making is being
developed
4. Modeling of intellectual decision support system
IDSS (figure 7) carries out the input, processing, storage and exchange of information. Information
sources in addition to ACS (measurement data) are: PDM-system (data and knowledge of similar
IDSS), accumulating information about the product within the life cycle, decision-maker (data of the
conditions for solving the problem), knowledge engineer (new and corrected data and knowledge), a
programmer (developed or adjusted software).
To model the desired states in accordance with established market priorities or regulatory terms of
reference, it is advisable to use the capabilities of databases on technical solutions or access to
knowledge bases. An excellent creative accumulator of non-standard technical solutions is TRIZ,
which includes a list of methods and methods for the preventive development of a technical system.
Inclusion of a set of typical principles for solving technical contradictions of TRIZ arising in the
design of new RAS, taking into account possible WWF, will reduce the time and resources of the
development team. Using the laws of the development of technical systems included in the expert
knowledge base will create the ability to control the drift of parameters through constructive
modernization or artificial aging as separate structural elements and the entire system as a whole. This
will have the necessary consumer effect on the sales market. The supplemented fuzzy classification by
TRIZ tools will allow not only to determine the technical state and consumer value of the system
under study at all stages of the SDS life cycle, but also to promote the development of the potential of
this system to a competitive product.
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External factors
Responsible appointment systems
Stimulating effect Responsiveness
Automated control system
Measurement data
Interface of interaction with the Intelligent decision support
automated control system system
Request and issue of data
Database Database Management Solver
System
Request and issue of
Recording and retrieving data
knowledge
Knowledge base Record and sample
Running and calculation results
Input and display of data
knowledge
The module of expert knowledge (formulas, decision
The module for estimating Module for shaping the
critical parameters based boundaries of the lead-out Knowledge
on the theory of fuzzy sets areas based on the theory of
rules, algorithms, membership functions, etc.)
and the fuzzy logic fuzzy sets Management
apparatus System
Input and display
The module for the multicriteria identification of the of knowledge
technical state of the RAS based on the production
rules system
Interface of interaction
with users and external
systems through local
networks and the Internet
Module for troubleshooting Troubleshooting module
based on the Bayesian based on a case-law
approach approach
The module for determining the numerical values of the
"degree of preference for a solution"
The module for forming the sequence of procedures for
diagnosing the components of RAS
Use Case Library
Prediction Diagnosis
A module for predicting the drift of critical parameters
based on a hybrid algorithm
The person making the decision, the attendants
Enterprise Information System
Figure 7. Structure of intellectual decision support system.
5. Conclusion
The proposed methods of fuzzy classification of the technical state at the stages of the life cycle of the
systems of responsible use differ from the existing ones in that it allows to control the so-called drift of
the functionally significant parameters of the system under study, influenced not only by the factors of
the development of scientific and technological progress, the social environment, but also by external
factors, having a destructive character. Complemented fuzzy classifications contain membership
functions that are applicable to each of the states of a technical system of responsible design that
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model different stages of its life cycle. Considering the reference to databases on current technical
problems of such systems, the principle of continuity is implemented. To do this, we use a
supplementary knowledge base with typical solutions of technical contradictions, laws of technical
systems development contained in TRIZ. This makes it possible to use the current databases as part of
a promising expert knowledge base, which will lead to original solutions for the design of new RAS
that meet not only all modern market requirements, but also create an additional competitive
advantage.
The proposed model for obtaining complex estimates of the "working capacity" of parameters by
the criteria of closeness of the values of the parameters to the boundaries of the tolerance fields and the
anomaly of the behavior of the parameter values within the boundaries of the tolerance fields allows us
to quickly detect and qualitatively estimate the insufficient availability and dangerous changes in the
critical parameters of the operable RAS. The received evaluations make it possible to take informed
decisions aimed at preventing potential failures to prevent emergency and emergency situations in the
operation environment, to carry out timely recovery and preventive measures (adjustment, additional
adjustment, debugging of the interaction of the component parts within the RAS, etc.) aimed at
increase in the availability of work and ensure the stable operation of the RAS, i.e. increase its actual
resource.
6. References
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[3] Leonenkov A V Fuzzy modeling in Matlab and fuzzyTECH. - S.Pb .: BHV-Saint-Petersburg,
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[4] Smirnov V A 2013 Malfunction searching in onboard control systems during acceptance
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[5] Korshunov G I, Prits V A, Frolova E A and Chupisov A E Ensuring the reliability of control of
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[6] Zadeh L A 1976 The concept of a linguistic variable and its application to approximate
reasoning(Moscow: World Press) p 165
[7] Zhirabok A N fuzzy sets and their application to decision making Soros Educational Journal. -
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[8] GOST 27.310 - 95 "Reliability in engineering. Analysis of species, consequences and criticality
of failures.Basic provisions »
[9] GOST R 51814.2 - 2001 "Method for analyzing the types and effects of potential defects"
[10] Zgurovsky М and Zaychenko Y 2012 A comprehensive analysis of the bankruptcy risk of
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