=Paper=
{{Paper
|id=Vol-2711/paper44
|storemode=property
|title=Cognitive-Impulse Model For Assessing Complex Technical Systems Survivability
|pdfUrl=https://ceur-ws.org/Vol-2711/paper44.pdf
|volume=Vol-2711
|authors=Vladimir Vychuzhanin,Nicolay Rudnichenko,Natalia Shibaeva,Yuriy Kondratenko,Igor Gritsuk
|dblpUrl=https://dblp.org/rec/conf/icst2/VychuzhaninRSKG20
}}
==Cognitive-Impulse Model For Assessing Complex Technical Systems Survivability==
https://ceur-ws.org/Vol-2711/paper44.pdf
Cognitive-Impulse Model For Assessing Complex
Technical Systems Survivability
Vladimir Vychuzhanin1 [0000-0002-6302-1832], Nicolay Rudnichenko1 [0000-0002-7343-8076],
Natalia Shibaeva1 [0000-0002-7869-9953], Yuriy Kondratenko2 [0000-0001-7736-883X],
Igor Gritsuk3 [0000-0001-7065-6820]
1Odessa National Polytechnic University, Odessa, Ukraine
vint532@yandex.ua, nickolay.rud@gmail.com, nati.shibaeva@gmail.com
2Petro Mohyla Black Sea National University, Mykolayiv, Ukraine
y_kondrat2002@yahoo.com
3Kherson State Maritime Academy, Kherson, Ukraine
gritsuk_iv@ukr.net
Abstract. The article proposes to assess the system components failure risk
during diagnostics based on the research methods analysis results and complex
technical systems models, in order to ensure their survivability. The authors de-
veloped a method for assessing the survivability of systems based on cognitive-
impulse modeling and interaction with a complex technical system, as with a
digraph subjected to the impulse action of simulation modeling impulses. The
created cognitive-impulse model allows us to evaluate the survivability, the de-
gree of structural and functional damage to the complex technical system com-
ponents and intercomponent connections. The developed cognitive-impulse
model of survivability assessment has flexibility - the ability to use the method
at any level of subsystem components failure risk assessment with their various
configurations; adaptability - the ability to adapt to changes in the configuration
of complex technical systems subsystems. The developed software allows to
obtain both numerical and graphical results of various classes technical systems
cognitive-impulse models studies and complexity. The developed cognitive-
impulse model effects over a complex technical system makes it possible to es-
tablish emergency and pre-emergency conditions for its subsystems.
Keywords: complex technical system, survivability, cognitive impulse model-
ing, software
1 Introduction
The structure and functional properties of complex technical systems (CTS) are de-
termined by numerous heterogeneous components interacting with each other, the
number of which can reach thousands. In CTS design and operation, the reliability
requirements for systems are constantly being tightened. This is associated with the
failures risk, accidents and disasters arising from the technical systems operation. The
disruption of such systems functioning is possible if the connectivity of their struc-
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons
License Attribution 4.0 International (CC BY 4.0). ICST-2020
tures is broken. The system cannot perform its functions when there are no interac-
tions between all or its vital components. From the point of view of the security con-
cept, CTS must be studied from the standpoint of reliability, survivability.
2 Description of Problem
Among the models used to assess CTS survivability, we can distinguish: normative
and descriptive approaches; probabilistic and deterministic models; fault trees; logi-
cal-probabilistic and analytical methods [1-8]. Methods associated with structured
objects and fuzzy knowledge are used to assess the survivability of CTS. In practice,
these methods are rarely found in their pure form. Therefore, one of the most common
survivability assessment systems is combined systems (for example, combining nor-
mative and logical-probabilistic approaches). The most developed methods are logi-
cal-probabilistic models and fault trees. Another rapidly developing area is cognitive-
simulation modeling [7,9,10].
CTS survivability assessment is impossible without taking into account the struc-
tural and functional relationships of their subsystems and components [11,12]. To do
this, the CTS failure risk assessment should take into account the division of systems
into subsystems (complexes, aggregates), components (components and parts). As
part of the subsystem, structurally and functionally completed components of the
system can be considered, the interaction of which should ensure its reliable opera-
tion. Each CTS component is associated with other components in a specific way.
Therefore, when assessing the CTS subsystems component failures risk it is necessary
to identify their interconnections and interactions, conducting a structural systems
analysis. When researching the CTS model, it is necessary to establish the features of
the system's functioning in various operating conditions and reduce the failure risk of
its subsystems and components. If we consider the problem of assessing the failures
risk in diagnosing CTS state from risk management theory view point [13,14], then
the corresponding model for assessing the survivability of a system should contain the
main components that affect its quality and functioning efficiency. Such an approach
in studies without detailing CTS, processes and phenomena occurring in them is a
system synthesis [15,16].
In order to ensure CTS survivability under various operating conditions, to suc-
cessfully investigate systems reliability during their transitions between different state
variants, further development of methods for assessing CTS survivability, taking into
account the structural, functional interactions and interactions CTS subsystems and
components [17- 20] is necessary. It is actual to develop methods and models, new
solutions for CTS survivability assessment informatization to ensure: flexibility - the
ability to use methods at any level to assess the subsystems components failure risk
with their various configurations; adaptability - methods must have the ability to
adapt when changing CTS subsystems configuration.
Thus, the solution of the systems ensuring the survivability problem upon receipt
of information about failures in their components and subsystems remains relevant in
the technical condition diagnosis.
3 Principles of constructing a cognitive-impulse model for
assessing complex technical systems survivability
In order to ensure CTS survivability, taking into account the structural and func-
tional interactions and interactions of components and subsystems, it is proposed to
use system synthesis based on a probabilistic-deterministic model that describes im-
pulse propagation effects on system components. CTS research experience shows that
at the stage of assessing the survivability of its components and subsystems, it is ad-
visable to represent them as an oriented graph, the vertices of which are components
(subsystems) [21] that have certain properties, and describe the interaction of compo-
nents (subsystems) using oriented graph edges. The survivability measure in this case
is the minimum number of CTS components (vertex connectivity [22]) or connections
(edge connectivity [22]), the failure of which under the influence of external influ-
ences leads to a breakdown in the system structure connectivity.The proposed method
uses complex cognitive-impulse modeling and interacts with CTS, as with a digraph
subjected to impulse effects of different modeling and distribution modeling simula-
tion impulses. The essence of this modeling is that at one of oriented graph vertices a
certain change is specified. This vertex actualizes the entire indicators system, i.e.
peaks associated with it. Passing along the vertices and edges of the oriented graph,
the impulse changes their states and changes itself (depending on the model, the im-
pulse impact modulus can change). In assessing CTS structural threats, the effect of
the unchanging striking modeling impulse (SMI) is used, when propagating through a
oriented graph that modifies the state of individual components (subsystems). Func-
tional threats are evaluated using a varying diagnostic modeling impulse (DMI),
which propagates through the oriented graph and does not affect the vertices.
The method uses the concepts of threats and risks. The threat is a dimensionless
off-scale assessment component vulnerability in terms system structural survivability
as a whole. CTS survivability is considered as a combination of two main factors -
CTS vulnerability as a complex structure and the likelihood of CTS certain compo-
nents failure as a result of external or internal adverse effects. Threat analysis reveals
vulnerabilities - “weaknesses” in the CTS structure. The basic concepts in the pro-
posed method are the concepts of potential structural and functional threats. A poten-
tial structural threat is a component position assessment in the CTS structure and its
role in the system topology. A functional threat also takes into account the position
and role of the component in the system, but from the point of view of potential
changes in the system’s operability, where operability means the probability that the
system will successfully complete tasks after exposure to adverse and damaging fac-
tors. CTS is considered to be affected by external influences. An increase in the load
can be observed not only in those system components that are influenced by external
influences, but also in the components interacting (connected) with them. It should be
noted that we are talking only about loads that are not normative, i.e. taken into ac-
count in the system design and operation. Short-term and powerful external (pulsed)
impacts can instantly and significantly reduce the reliability indicators of individual
components and the entire system as a whole, but CTS is able to maintain its operabil-
ity.
4 Model of striking effects spread on CTS
In the CTS digraph, vertices correspond to components (complexes, systems, sub-
systems, elements), and directed arcs correspond to one of the types of intercompo-
nent connections (transfer of matter, information, or energy). Many vertices (compo-
nents) of a oriented graph V – ( V = {vi }, i = 1, N ) . Pair vertices vi – oriented graph
edge – component interconnection (CI). CI set A − ( A = {a j }, j = 1, M ) . SMI impacts
are determined by the pulse vector imp i, j t ( ) for discrete time t = 0,1,2,3... Each
component (CI) characterized by specific properties, the combination of which deter-
mine the state of the components (CI). The qualitative state of the CTS components is
expressed by the state functional
vi = ( Fvi , a ji , aij , H mvi (t ), K vi ) , (1)
where Fvi – current component performance; a ji , aij – quality state of incoming and
outgoing for a component CI; K vi – component structural striking degree coefficient
(SSDC); H mvi (t ) – amplitude change SMI coefficient, passing through the CTS com-
ponent
imp m (t )
H mvi (t ) = i +1, j
imp mi , j (t )
, (2)
where impmi , j (t ) , impmi +1, j (t ) – SMI amplitude value at the CTS component input and
output
wv (t + 1)
K vi = H mvi (t ) i
wvi (t )
(3)
where wvi (t ) , wvi (t + 1) - the value of the component (vertex) weight at time points t ,
t + 1 as a result of exposure to SMI.
Qualitative state of CI CTS is expressed by the state functional
a j = ( Fa j , vij , v ji , H m j (t ), K a j ) ,
a
(4)
where Fa j – CI current performance; vi , j , v j , i – quality condition of components at
a
the CI beginning and end; K a j – SSDC CI strike; H m j (t ) – SMI change amplitude
coefficient passing through CI.
impmi , j+1 (t )
H m j (t ) =
a
, (5)
impmi , j (t )
where impm (t ) , imp mi , j+1 (t ) – amplitude value SMI at input and output CI.
i, j
wai , j (t + 1)
K a j = H m j (t )
a
, (6)
wai . j (t )
where wai , j (t ) , wai , j (t + 1) - weight value CI (edges) at time points t , t + 1 as a result
SMI exposure.
The structural vulnerability vertices (edges) structural damage degree coefficients
(SVC) reflect the level of threat assessment of the system, allowing us to rank the
CTS components according to the degree of structural significance and highlight the
least reliable.
SMI impact on the vertex (edge) of the graph at a discrete time t determined by
wvi ( ai , j ) (t )
1 − impi (i , j ) (t ) = , (7)
wvi ( ai , j ) (t − 1) K vi K a j
where imp i ( j ) (t ) – impulse vector for vertex (edge) with number i ( i, j );
wvi ( ai , j ) (t ) , wvi ( ai , j ) (t − 1) – value edge (vertex) weight at a time t and at the previ-
ous moment in time t − 1 . Oriented graph vertex weight wvi (t ) - the magnitude of
its reliability for the top vi vertex. Edge weight wai , j (t ) is determined by a numeri-
cal value from 0 to 1 and is equal to the proportion of SMI from vi to v j . When
DMI passes through the CTS components at time t, the impulse actions are deter-
mined for the connected components:
• sequentially (to the top vi ), t+1 (after passing v j ), t+2 (after going through the
vi and v j )
imp (t + 2 ) = imp (t +1) v j = imp (t ) vi v j . (8)
• in parallel (before passing vi and v j ), t+1 (passing vi and v j ), t+2 (after pass-
ing the junction point of the oriented graph)
imp (t + 2) = imp (t ) vi + imp (t ) v j = imp (t )(vi + v j ) . (9)
Similarly, one can obtain relations for more complex series-parallel structures.
Representation of CTS in the form of a oriented graph and formalization of the ex-
ternal influence on the system as an impulse action (1) - (6) defines a model of the
distribution of damaging effects across the system. Consider the impulse effect on the
CTS, the components of which have survivability and bonds equal to unity, using
SMI. The impact simulates the defeat of the object number i by a force impact 1.0 and
is equivalent to the complete defeat of the component and its failure. In a single sys-
tem, struck by a single impulse, the connections between the vertices of the oriented
graph are also single, so the SMI will propagate through the CTS until it disables all
available components. This is a case of the “worst case scenario” for a single hit.
Consequently, the criticality of an element (and with it the degree of damage to the
system) can be estimated by damage volume it causes to the CTS. As an example of
such an impact, consider a oriented graph (fig. 1). We sequentially act on each of its
vertices with a single impulse and follow the propagation of the impulse along the
oriented graph. We believe that in Fig. 1, the final phase of the impact of a single SMI
falls on the peak of V9.
Consider the scenario of the defeat of a system unit by a single impulse (assuming
that the vertices of the oriented graph have unit conductivity). The pulse will propa-
gate through the system until it disables all available components. Hence, the criticali-
ty of the edge (and with it the vulnerability of the system) can be estimated by the
extent to which the damage to the CTS as a whole causes the damage. We sequential-
ly act on each edge of the oriented graph with a single SMI and trace the propagation
of the impulse along the oriented graph. For example, Fig. 1 shows the final phase of
an impulse propagation in the event of a connection failure E8, 10 (failed digraph
connections are indicated by a dotted line). Peak and link structural vulnerability as-
sessments provide, as a first approximation, a weight estimate of the vertex or link
significance to ensure CTS survivability. Numerically, such an estimate is the greater,
the more vulnerable system component.
Fig.1. CTS oriented graph
As an example, lets study a numerical simulation of the oriented graph vertices
(edges) strike shown in Fig. 1. As a result, we obtained the following SSDC values of
oriented graph vertices.
Table 1. The coefficients of the oriented graph vertices damage structural degree
V1 V2 V3 V4 V5 V6 V7
0.71 0.72 0.7 0.6 0.6 0.64 0.58
V8 V9 V10 V11 V12 V13 V14
0.58 0.52 0.23 0.06 0.2 0.06 0.06
In accordance with the obtained results (Table 1) in the oriented graph under con-
sideration, the vertices V1, V2, V3 are structurally important, V4, V5, V6, V7, V8,
V9 are less structurally significant, the others are V10, V12, V11, V13, V14.
It should be noted that a change in a single DMI in the case of passing through the
oriented graph vertices, provided that vertices weights are numerically equal to the
assessment of their performance, will be numerically equal to the overall performance
of the system. Therefore, modeling the passage through CTS of a single DMI allows
us to evaluate system performance according to the above criteria.
5 Cognitive impulse simulation software
The following tools were chosen to simulate DMI effect on the system: presenta-
tion of the original models in JSON format; high level Python programming lan-
guage; graphviz graph visualization tools; make utilities; Debian GNU / Linux distri-
bution tools. The simulation was carried out on the basis of the Debian GNU / Linux
distribution, with widespread use of shell software (makefiles, bash scripting lan-
guage).
The utility uses special make-files, which indicate the dependencies of the files
from each other and the rules for satisfying them. Based on the information about the
time of each file last change, make determines and runs the necessary programs.
Technologically, the modeling process using the make-utility looked as follows: the
original model was formulated in terms of the JSON format, then transferred to the
Python program input. Program result was an array of tables in *.csv format and files
in *.dot language format. Then the array of dot-files using the make utility was batch
transferred to the input of the graphviz program, the result of which was an array of
the corresponding diagrams in *.png format. Analysis of the results was carried out by
means of Calc Libre Office. To simulate the damaging and simulating impulses, two
separate scripts were created, each of which contains a set of common functions. Both
programs include the main part, auxiliary functions and the SMI or DMI simulation
module, depending on the program purpose. The set of auxiliary functions is the same
for both programs and contains the following functions: GetJson - analyzes the JSON
file and builds a CIM based on it; DotFileDot - generates a dot-file with damaged
nodes and passing pulses; DotFile - generates a sequence of dot-files for graphviz;
Meet_In_Other - performs auxiliary functions (search for a node intersecting with
vectors of other nodes); TablesToCsv - saves the results of the program to an array of
csv tables. The HitPulse and DiagPulse routines, respectively, are used to model the
effects of DMI. Programs are organized in such a way that they can be called both on
their own (in stand alone mode) and as part of more complex software systems (in the
form of plug-in libraries of the Python language).
Further processing of the results is performed using the GNU make utility and the
grpaphviz visualization utility. A file in the dot language format is a text description
of the graph in which the design of the nodes and the links between them is specified.
To facilitate batch processing of the received dot-files, a make-file was used. Such a
scheme made it possible to generate as all files at once (using the make directive ei-
ther make all), or separately (make_st for SMI, make_fun for DMI), as well as to
clean the directory before starting a new modeling cycle (make clean). In addition to
the GNU make utility, GNU core utilities were widely used in modeling, mainly for
converting csv tables to a form convenient for visualization using Calc Libre Office
tools. The main uses were the GNU awk table data processing language and the paste
utility included in the GNU coreutils package.
The logical concept of the developed software in Python is based on a microservice
architecture with the goal of providing scalability and distribution of computational
operations for complex systems with a large number of elements and interconnec-
tions. The user interface was developed by means of the QT Designer graphical envi-
ronment with the connection of the pyQt library; it is a *.ui format file that stores the
hierarchical structure of widget placement for processing user actions (data input,
initialization of computational processes and graph construction) in a declarative
form. The program code is implemented using the PyCharm IDE, based on 6 separate
classes: main (the entry point to the program, contains all external dependencies and
binds the structure of other classes), process (performs the functions of processing
input data from a json file and importing them into dictionary structures), controller
(provides the logic for processing user interaction with interface elements), calculate
(implements the calculation functions for performing the simulation), visualize (im-
plements the rendering and saving of the simulation results in the form of graph),
logger (logs user operations performed in the program and simulation results into
separate text files in *.log format). The diagram of the main options for using the
simulation system is shown in Fig. 2. The user can import data by starting the json file
parsing procedure. Set parameters for performing calculations, in particular, re-
strictions on the number of elements, degree of nesting, or calculation time, as well as
parameters for generating graph images (image resolution, font type and size, color of
edges and vertices). Set parameters for performing calculations for a given system
structure for functional and structural damage, viewing calculation results in the form
of diagrams and tables, saving and loading a model (data serialization and deserializa-
tion), saving generated action logs during the program operation, saving graph models
in the process modeling, as well as exporting data in tabular form in *.xls or *.csv
formats.
Fig.2. Simulation system use cases
As an illustration CMI software use is shown in Fig. 3. It shows the CTS (commu-
nication network of some control system), depicted as a oriented graph, and Fig. 4
shows modeling signal passage. The network should ensure the passage of the model-
ing signal from V6 to V2. A signal is sent to V6, which should reach V2. The main
event S is the signal passing from the V6 to the V2. Intermediate events Si, i =
{1,2,3,4,5,6} - signal not passing to the V2.
Fig.3. Oriented graph of the communication network management system
Fig.4. Modulating pulse movement
Thus, at a basic level, the structural threat to survivability is assessed by the se-
quential impact of SMI on each oriented graph vertices and then SSDC is calculated.
The functional threat to the survivability of each oriented graph vertex is estimated as
total DMI value ratio that passed through a CTS and the total DMI value during the
sequential each vertex model failure. The ratio of the obtained values allows to obtain
the CTS functional degree affection coefficient.
6 Cognitive impulse CTS survival assessment research
To conduct a CIM of assessing CTS survivability, an air conditioning system
(ACS) operating in the “summer” mode of operation was selected as an object (Fig.
5). The system consists of units: Cm - compressor; E - electric motor; K is the capaci-
tor; V is the evaporator; R1, R2 - control valves; N is the pump; Cn - control system;
A - air cooler. A distinctive feature of the model under consideration is the non-
hierarchical structure of the connections of its aggregates - the presence of a closed
loop "Cn - K - R1 - V", as well as the presence of two types of connections - resource
(solid lines) and information (dashed).
Fig. 5. ACS oriented graph in summer mode
To calculate SSDC, the vertices of the cognitive-impulse ACS model were sequen-
tially exposed to SMI. Changes in vertex states for the initial and final stages of SMI
propagation over the oriented graph are shown in Fig. 6.7 (disabled units are indicated
by a dotted line). For aggregate Cm SVC ks = 0.667 (Table 2).
Fig. 6. Initial stage of SMI exposure to Cn CIM ACS
Fig. 7. The final stage of SMI exposure to Cn CIM ACS
Table 2. The coefficients of the ACS aggregates destruction structural degree
Unit kvi
A 0.111
Cm 0.667
Cn 1.000
E 0.778
K 0.667
N 0.778
R1 0.667
R2 0.222
V 0.667
Based on the calculated SSDC values and the reliability of the units, the values of
their structural failure risk were calculated (Table 3).
Table 3. ACS aggregate ranking by structural risk level of ACS unit’s failure
Unit rs Unit rs
Cm 163,55 R1 9,13
K 76,11 Cn 3,15
N 46,99 R2 3,04
E 32,11 A 2,28
V 18,26
In order to assess functional threats (FT) and failure risk (FR) of aggregates within
CIM, we expose the ACS digraph to DMI and analyze its distribution. Let us assume
the conditional efficiency of all model vertices equal Fa = 0.9. Having ranked the
results on the risk of failures (Table 4), we establish the most important ACS units for
functioning. The system operability is dependent on the state of the units E and Cm.
Table 4. ACS aggregate ranking by FT and FR
Unit Θf rf 10 −3 Unit Θf rf 10 −3
Cm 0,99999998 0,24533 V 0,34072216 0,00933
E 0,99999998 0,04129 R2 0,00101010 0,00001
N 0,67367395 0,0407 Cn 0,00101010 0,00001
K 0,67367395 0,07691 A 0,00101010 0,00002
R1 0,34072216 0,00466
In conducting the ACS studies, eighteen scenarios and one hundred thirty separate
state diagrams were considered for its nine units. As a result of cognitive-impulse
modeling, the values of structural and functional threats and risks were obtained, vul-
nerabilities in the CTS were identified. The study of ACS cognitive-simulation mod-
eling allowed us to determine structural and functional vulnerabilities and determine
the “weight” and “contribution” of aggregates to the overall survivability of the sys-
tem, taking into account its layout and structure. The introduction of a reliability indi-
cator allows us to refine the ranking of aggregates by risk and obtain data that can be
used in the operation of the system and at the stage of its maintenance, modernization
and design. In accordance with the described impulse action on the oriented graph,
one can introduce various criteria (signs) for the system to reach the limit state. It
should be considered that the CTS is in a critical state if the reliability of one or more
of the most significant system components is below a certain acceptable level (critical
level of element reliability). Such a criterion unambiguously separates the CTS com-
ponent subcritical and supercritical state.
7 Conclusion
The proposed method for assessing CTS survivability is based on integrated cogni-
tive-impulse modeling and interaction with the system, as with a oriented graph sub-
jected to impulse action of different modeling impacts of different propagation pat-
terns and changes. The created cognitive-impulse model makes it possible to evaluate
the survivability of components, intercomponent connections and CTS as a whole, to
determine the degree of structural and functional damage to CTS components and
intercomponent connections. The developed cognitive-impulse model of survivability
assessment has flexibility - the ability to use the method at any level of assessing the
subsystems components failure risk with their various configurations; adaptability -
the ability to adapt to changes in the configuration of CTS sub-systems. The devel-
oped software of the cognitive-impulse model for assessing CTS survivability allows
one to obtain both numerical and graphic results models studies systems of various
classes and complexity.
Reference
1. Atamanyuk, I., Kondratenko, Y.: Computer's Analysis Method and Reliability Assessment
of Fault-Tolerance Operation of Information Systems. In: ICT in Education, Research and
Industrial Applications: Integration, Harmonization and Knowledge Transfer. Proceedings
of the 11th International Conference ICTERI-2015 // S.Batsakis et al. (Eds.), CEUR-WS,
Vol. 1356, pp. 507-522 (2015)
2. Atamanyuk, I., Shebanin, V., Kondratenko, Y., Volosyuk, Y., Sheptylevskyi, O.,
Atamaniuk, V.: Predictive Control of Electrical Equipment Reliability on the Basis of the
Non-linear Canonical Model of a Vector Random Sequence. In: Proceedings of the
International Conference on Modern Electrical and Energy Systems, MEES 2019,
8896569, рр. 130-133 (2019)
3. Hakobyan, A., Aldemir, T., Denning, R., Dunagan, S., et al.: Dynamic generation of
accident progression event trees. In: Nucl. Eng. Des., №238, рр. 3457–3467 (2008). doi:
10.1016/j.nucengdes.2008.08.005
4. Ibánez, L., Hortal, J., Queral, C., Gómez-Magán, J., Sánchez-Perea, M., Fernández, I., et
al.: Application of the integrated safety assessment methodology to safety margins.
Dynamic event trees, damage domains, and risk assessment. In:Reliab. Eng. Syst. Saf.,
№147, рр. 170–193 (2015). doi: 10.1016/j.ress.2015.05.016
5. Mandelli, D., Yilmaz, A., Aldemir, T., Metzroth, K., and Denning, R.: Scenario clustering
and dynamic probabilistic risk assessment. In: Reliab. Eng. Syst. Saf., № 115, рр. 146–160
(2013). doi: 10.1016/j.ress.2013.02.013
6. Ryabinin I.A.: Nadezhnost' i bezopasnost' strukturno-slozhnykh sistem. SPb.: Politekhni-
ka, (2000)
7. Vychuzhanin, V.V., Rudnichenko N.D.: Metody informatsionnykh tekhnologiy v diagnos-
tike sostoyaniya slozhnykh tekhnicheskikh sistem. Monografiya, Odessa (2019)
8. Tsvetkov, V.YA.: Slozhnyye tekhnicheskiye sistemy. In:Obrazovatel'nyye resursy i
tekhnologii, №3(20), рр. 86-92 (2017)
9. Rudnichenko, N. Vichuzhanin, V.: Information cognitive model of technological interde-
pendence of marine technical recourses. In: Informatics and mathematical methods in sim-
ulation, Vol.3, №3, рр. 240-247 (2013)
10. Vychuzhanin, V. Rudnichenko, N., Zenkin, E.: Cognitive Model of the Internal Combus-
tion Engine. In: SAE International, 2018-01-1738, рр. 1-10 (2018). doi:10.4271/2018-
01-1738
11. Vychuzhanin, V. Rudnichenko, N., Sagova, Z., et al.: Analysis and structuring diag-
nostic large volume data of technical condition of complex equipment in
transport. In: Machine Modelling and Simulations (2019). doi:10.1088/1757-
899X/776/1/012049
12. Dorozhko, I. V., Tarasov, A. G., Baranovskiy A. M.: Otsenka nadezhnosti strukturno
slozhnykh tekhnicheskikh kompleksov s pomoshch'yu modeley bayyesovskikh setey do-
veriya v srede GeNIe. Intellectual Technologies on Transport, № 3, рр. 36-45 (2015)
13. Zhang, R., Brennan, T.J. and Lo, A.W.: The origin of risk aversion. In: PNAS Proceedings
of the National Academy of Sciences of the United States of America, Vol. 111, No.50,
pp. 17-82 (2014)
14. Shevtsov, U.D., Dudnik, L.N., Aref'yeva, S.A., Fadeyev, Ye.D.: Prognozirovaniye par-
ametrov tekhnicheskogo sostoyaniya dvigateley energeticheskikh ustanovok. In: Poli-
tematicheskiy setevoy elektronnyy nauchnyy zhurnal Kubanskogo gosudarstvennogo
agrarnogo universiteta, № 132, рр. 508-517 (2017)
15. Kurdyumov S.P., Malinetskiy G.G.: Sinergetika i sistemnyy sintez. Novoye v sinergetike:
vzglyad v tret'ye tysyacheletiye, M.: Nauka (2002)
16. Mosleh, A., Groen, F., Hu, Y., Nejad, H., Zhu, D., and Piers, T. Simulation Based
Probabilistic Risk Analysis Report. Center for Risk and Reliability, University of
Maryland (2004)
17. Vychuzhanin, V., Rudnichenko, N., Shibaeva, N., Konovalov, S.: Devising a method for
the estimation and prediction of technical condition of ship complex systems. In: Eastern-
European Journal of Enterprise Technologies, Vol. 84, № 6/9, рр. 4 -11 (2016)
18. Vichuzhanin, V., Rudnichenko, N.: Development of the neural network model for predic-
tion failure risk’s of complex technical systems components. In: Informatics and mathe-
matical methods in simulation, Vol. 6, №4, рр. 333-338 (2016)
19. Pohorletskyi, D., Gritsuk, I., Zenkin, E., Bulgakov, N., Golovan, A., Vichuzhanin, V.: The
Complex Application of Monitoring and Express Diagnosing for Searching Failures on
Common Rail System Units. In: SAE International, 2018-01-1773, рр. 1-10 (2018).
doi:10.4271/2018-01-1773
20. Vichuzhanin, V., Rudnichenko, N., Mateichyk V., Polyvianchuk A.: Complex Technical
System Condition Diagnostics and Prediction Computerization. In: CMIS-2020 Computer
Modeling and Intelligent Systems, Сeur-ws.org. 2608 (2020)
21. Kasti, D.: Bol'shiye sistemy. Svyaznost', slozhnost' i katastrofy, M.: Mir (1982)
22. Kochkarov, A.M., Kochkarova, A.N., Khapayeva, L.K.: Modelirovaniye fraktal'nogo
razvitiya struktur prosteyshimi predfraktal'nymi grafami. In: Izvestiya YUFU. Tekhnich-
eskiye nauki, рр. 90-101 (2016)