=Paper=
{{Paper
|id=Vol-2864/paper39
|storemode=property
|title=Optimization of Data Transmission System Information Parameters for Complex Technical System's State Diagnosing
|pdfUrl=https://ceur-ws.org/Vol-2864/paper39.pdf
|volume=Vol-2864
|authors=Vladimir Vychuzhanin,Natalia Shibaeva,Nickolay Rudnichenko,Alexey Vychuzhanin
|dblpUrl=https://dblp.org/rec/conf/cmis/VychuzhaninSRV21
}}
==Optimization of Data Transmission System Information Parameters for Complex Technical System's State Diagnosing==
https://ceur-ws.org/Vol-2864/paper39.pdf
Optimization of data transmission system information
parameters for complex technical system’s state diagnosing
Vladimir Vychuzhanina, Natalia Shibaevaa, Nickolay Rudnichenkoa, Alexey Vychuzhanina
a
Odessa National Polytechnic University, Shevchenko Avenue 1, Odessa, 65001, Ukraine
Abstract
Complex technical systems are often operated in tense, abnormal conditions, with a large
number of correlated parameters. Timely and high-quality diagnostics, including remote
diagnostics, of complex technical systems components can increase their reliability. To
successfully solve the problem of such systems trouble-free operation in abnormal operating
modes, it is necessary to maximize the use of information technologies with software and
hardware diagnostic modules. In order to make a reasonable choice of system’s information
parameters for diagnosing complex technical system’s state it is necessary to solve the
problems of multi-parameter, multi-criteria optimization data transmission systems. Such
task aimed for increasing the speed, minimizing the error and risk of system’s elements
failure, and maximizing the transmitted information protection. The objective function of
optimizing the information parameters of the data transmission systems for remote
diagnostics is its multi-parameter optimization of the variables that affect the remote
diagnostics of all system’s efficiency. Solving the problem of optimizing the remote data
transmission systems for remote diagnostics information parameters based on the developed
model allows us to find several Pareto-optimal solutions for quality indicators, which are
affecting the efficiency of the data transmission systems for remote diagnostics. Basic
parameters and characteristics of the data transmission system are the optimal number of
devices types for monitoring complex technical systems technological parameters, topology
efficiency, throughput, speed, error of the data transmission system, the risk of component
failures and the information protection effectiveness in the remote diagnostics system.
Keywords
Complex technical systems, diagnostics, information parameters, data transmission system,
multi-parameter, multi-criteria optimization, Pareto-optimal solutions, throughput, error.
1. Introduction
Considering the problem of diagnosing complex technical systems (CTS), these are objects often
with multimodality, uncertainty of behavior, a large number of correlated parameters, functioning in
tense, abnormal modes [1–3]. The purpose of CTS state diagnosing is to improve reliability, prevent
accidents that cause shutdown and damage to systems, in many cases accompanied by human
casualties and damage to the environment [4-6].
2. Description of Problem
The analysis of technical solutions that improve the reliability of the CTS operation showed that
timely and high-quality diagnostics, including remote diagnostics, of the CTS components can
CMIS-2021: The Fourth International Workshop on Computer Modeling and Intelligent Systems, April 27, 2021, Zaporizhzhia, Ukraine
EMAIL: vint532@yandex.ua (V. Vychuzhanin); nati.sh@gmail.com (N. Shibaeva); nickolay.rud@gmail.com (N. Rudnichenko);
vint532@gmail.com (A. Vychuzhanin)
ORCID: 0000-0002-6302-1832 (V. Vychuzhanin); 0000-0002-7869-9953 (N. Shibaeva); 0000-0002-7343-8076 (N. Rudnichenko); 0000-
0001-8779-2503 (A. Vychuzhanin)
© 2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
improve the reliability of systems and their operation efficiency [4,7]. To successfully solve problem
of CTS accident-free operation in abnormal operating modes, it is necessary to maximize the use of
information technology with software and hardware diagnostic modules [4,8].
These can be elements and devices in Distributed Industrial Control Systems (DICS) serving
technological objects of "critical infrastructure" (energy, transport, mining and processing of minerals,
communications, national security, life support systems, etc.).
The system allows combining into a single system the lower level of technological subsystems for
collecting, storing, transmission and distributing information with the upper level of control,
monitoring and diagnostics.
To effectively perform their functions to maintain the high quality of processes and the specified
characteristics of channels and paths for transmitting technological and other information, the CTS
state diagnostic system must provide [9]:
large flows information processing generated by a significant number of controlled
parameters;
increasing the reliability and reducing the redundancy of information at its processing stages;
the speed and functioning of the system in real time, namely, the processing of information
arriving at its inputs (reception and transmission of data between system’s functional elements,
synchronization of the information further analysis process) at a rate not lower than the rate of
events development in the CTS.
In order to make a reasonable choice of the data transmission system (DTS) topology for
diagnosing, its high-quality operational parameters, it is necessary to solve the problems of multi-
parameter, multi-criteria optimization of information parameters aimed at increasing the speed,
minimizing errors and the risk system elements failures, and maximizing the transmitted information
protection [10]. The use of well-known optimization methods for solving practical problems of multi-
criteria, multi-parameter optimization [10 - 12] should take into account: large dimension of problems
(tens and hundreds of variables and constraints); topological complexity of the optimized function;
significant computational costs; the need to solve the problem in a multicriteria formulation, the use
of unrelated models. Thus, ensuring the quality of the functioning of the diagnostic information
system (including the remote one) with optimal parameters is an urgent scientific and technical
problem. The aim of the work is to maximize CTS remote diagnostics system efficiency by
optimizing its information parameters.
3. Data transmission system for complex technical system state remote
diagnostics development
Operation of a single open platform for CTS remote diagnostics systems should be based on ISO
standards in the field of "Condition monitoring and diagnostics of machines" [13]. The use of a
standard base allows for the unification of approaches to obtaining information from primary
measuring instruments and its transmission.
In Fig. 1 shows the block diagram of the DTS is shown for diagnosing the state of CTS in a
distributed information management system.
Any ship's CTS can be selected as an object of remote diagnostics.
The remote diagnostic (RD) system consists of two subsystems, one of which is installed on the
ship, the other in the cloud or the coastal dispatch center.
The main method for diagnosing the state of CTS is the collection, processing and analysis of
measurement information.
In order to optimize the information parameters of the DTS system of CTS ship's state, data
transmission system model shown in Fig. 2.
Information transmission is carried out based on the use of one of the standards for digital
information transmission systems IEEE 802.15, WiMax, IEEE 802.22, UMTS, LTE.
The system parameters are selected in such a way as to ensure a certain level of information
transmission quality.
The accumulated diagnostic data of the CTS condition is sent in the form of reports to the coastal
diagnostic center.
Figure 1: Block diagram DTS for diagnosing the state of CTS in a distributed information
management system
Figure 2: Block diagram of the DTS model for diagnosing the state of the ship's CTS
The quality of the DTS for RD system model is define by a set of characteristics that affect the
efficiency of its operation: the topology (Fo); throughput (B); performance (T∑); permissible data
transmission error (σ); efficiency of information protection in the system (ZΣ), risk of DTS device
failures in RD(RΣ).
We assume that the model describing the RD system is linear, with both deterministic and
stochastic lumped parameters.
The set of quality indicators of the model can be written in the vector form, the coordinates of
which are particular indicators, and their specified values must be ensured (increased, improved to the
desired level)
Q {{F0 },{T },{B},{ },{R },{Z }} (1)
The generalized indicator of the quality of the functioning of the DTS in RD system is determined
by the results of information parameters optimization, as well as its reliability characteristics. The
objective function of optimizing the information parameters of the DTS in RD model of the CTS state
is its variables multiparameter optimization that affect system’s efficiency.
Data transmission system topology indicators optimization objective function can be presented is a
such form
( F ) max ( Fo ) max ( D , B , C ) , (2)
D Do D Dmin DDmax , К К o К К min К К max , C C o C 0 C C o
where D is length of data transmission paths;
К is compact structure of the system;
C is degree of system structure centralization;
D0 , К 0 , C0 are normalized partial criteria indicators of the system topology efficiency obtained by
reducing the indicators D, B, C to dimensionless form.
Objective function of optimizing the response time of the data transmission system RD
p
(T ) max (T ) max (Т S , TKO ,TC1 , TK , TPD ,Т KС ,Т PK ,Т DK , Т S ,Т LC ,Т С 2 ) ,
noi l
(3)
l 1
T TSnoi T TSomin TSo TSomax , T TKO T TKOmin TKOTKOmax , T TC1 T TC1min TC1 TC1max ,
T T T T
PK PK min , T T T T T T ,
TPK TPKmax , T TDK T TDK TDK TDK С2 С 2 min С2 С 2 max
T TK T TK TK TK , T T T T T T
min max
min max
, T T T T T T ,
LC LC min LC LC max S S min S S max
T T KC T T KC T KC T KC min max
(T ) min (TZ ) ,
T T Z T T Z min T Z T Z max
(TK ) max ( n , TL ) ,
T T L T T Lmin T L T Lmax ,
(TLC ) max ( BLC , f ) , (TS ) max ( BS , f ) , (T K С ) max ( B K С , f ) ,
( B ) max ( В LC , В S , В KC ) ,
B BLC B BLC BLC BLC , B BS B BS BS BS , B B KC B B KC B KC B KC
min max min max min max
,
Т S noi is technological parameters control devices performance;
where
TKO is controller performance;
TC1 TC2
, are server performance;
TK , TDK are encoder and decoder performance;
TPD is transmitter performance;
TPK is receiver performance;
TLC is processing speed of network streams in the receiving local network;
TS is processing speed of network packets in the receiving transit gateway;
TZ is signal delay at redundant nodes;
f is network packet size;
ВLC is host local network bandwidth;
ВS is receiving transport gateway throughput;
ВKC is communication channel capacity;
n is code combination length;
Т L is time required to transmit the codeword.
Objective function of RD system data transmission error
( ) min ( S , SPD ),
n0
(4)
S
, SPD
y(t) - y (t) , S 0;1 , SPD0;1,
n0
X MV 100 Y
no
where is relative measurement error;
S n0 are technological parameters control devices primary converters errors;
SPD are errors arising from data loss of the data transmission system;
X MV is measured value;
y (t ) is perfect signal;
y (t ) is real signal;
Y is signal variation range;
t is signal observation time ( o N ) t t t
The task of optimizing information protection is to ensure the maximum level of security with a
minimum risk from the likelihood of hacking - the data transmission system, i.e.
( Z ) min ( R HACK ,Т а ) , ( Z ) max ( N f ) (5)
N f N fo Ta Tao , RHACK p HACK Н , p HACK pi p a pb pc pd ,
at ,
R
where HACK is multiplicative criterion of risk from the probability of hacking the data
transmission system;
p HACK is the probability of an information system being hacked, determined on the basis of expert
data;
pi is the probability that this information system falls into the list of possible targets and such on
the information system;
pa is the probability that the system will be selected from the list and attacked;
pb is the probability that bordering technologies will be hacked;
pc is he probability that attacks will intensify;
pd is the probability that system will be harmed;
Н – potential damage from information security breaches;
Nf
is the number of functions that characterize the functionality of the data transmission system;
Ta is average time of access to data transmission system protection objects;
Nf0 T
and a 0 are specified restrictions on the functionality and performance of the data
transmission system.
RS n 0
The risk of RD structure failure is determined by the risk of CTS ( ) parameter control device
RSPD
failure, as well as the average risk of the data transmission system ( ).
Objective function of the system RD devices failure risk
( R) min ( R , RS , RSPD , pS , HS , Ri , P( Si ))
n0 n0 no
(6)
CTS parameter monitors failure risk
R Sn 0 p Sn 0 H Sn 0
, (7)
p
where S n 0 is the probability of CTS parameter control devices failure, determined on the basis of
expert estimates;
H Sn 0
is damage from failure of CTS parameter control devices;
Ri is contingent transmission risk;
P( Si ) is conditional transmission error probability.
Conditional risk in data transmission is determined by the sum of error probabilities
m
Ri l ij p( S j / S i )
i 1 , (8)
Sj
where is erroneous signal;
S i is original (correct signal);
lij
are losses arising from an erroneous decision due to a system error;
p( S j / Si )
is error probability.
4. Experiments and results analysis
The solution to the optimization problem of the CTS state information parameters DTS in RD is
based on the use of the developed optimization models. The goal is information DTS in RD
effectiveness indication.
The model is designed to optimize the information parameters of the DTS in RD of CTS system in
terms of the diagnostic system efficiency.
The method used, which makes it possible to study the developed model and optimize the
information parameters of the DTS in state RD system, is based on the presence of many conflicting
requirements for such system.
Optimal solutions to a multicriteria problem should be sought only among the elements of
alternatives set. In this area, no criterion can be improved without deteriorating at least one of the
others.
The complexity of solving multicriteria optimization problems is that the criteria compete with
each other.
In most practical problems, the search for a more preferable solution according to one criterion
leads to the fact that the solution becomes less preferable according to another criterion, i.e. solutions
are incomparable.
The problem can be solved by applying the Pareto optimality principle [14,15]. The property of the
optimal Pareto set is the ability to “reject” from the set of alternatives obviously unsuccessful ones
that are inferior to others in all criteria.
As a result of solving the problem of optimizing information parameters, a set of alternative
solutions is determined that correspond to the Pareto optimality principle and satisfy the set
constraints.
The statement of the problem of multicriteria optimization includes three components, namely, the
set of possible solutions, the vector criterion and the decision maker preference relation [10]. This
strategy for solving optimization problems significantly differs from the known approaches of
nonlinear programming, has a higher efficiency and provides significantly wider possibilities.
The sequence of the RD system information parameters optimization includes the following
stages:
determination of a set of independent parameters, as well as conditions that determine the
permissible values of the variables;
obtaining the objective function as a measure of the quality of the optimization object with
the given variables;
choice of method and solution of the optimization problem.
Thus, the formulation of the optimization problem for the information parameters of the RD
system will make it possible to develop a model for multi-criteria, RD system multi-parameter
optimization.
To study the model of multicriteria and multiparametric optimization of DTS in RD information
parameters, one can use algorithms implemented in the free software IOSO 3.3 [16], based on the
technology of constructing a response surface.
A distinctive feature of this technology is the high efficiency of finding the optimal solution in the
study of the DTS in RD system, modeled at high levels of complexity and hierarchy, including the
achievements of mathematical modeling (2- and 3-dimensional problems) and the ability to quickly
integrate with such computational packages as ANSYS, NASTRAN, TaskFlow, Star - CD,
FineDesign, etc.
The program implements an optimization algorithm that does not belong either to gradient
methods of nonlinear programming, or to genetic ones.
Therefore, unlike genetic algorithms, it has a high convergence rate (the minimum required
number of calls to the user model) and is characterized by the fact that:
no starting points are needed to start work;
the algorithm allows to successfully solve optimization problems in the presence of non-
differentiability, noisiness, local non-computability of objective functions and limited parameters;
it allows us to find several Pareto-optimal solutions within the framework of one task;
has good ability to find the global extremum on multi-extreme problems.
The program is designed for the numerical solution of problems of multicriteria parametric
optimization of complex functional dependencies in the presence of functional constraints and
operates taking into account the objective functions (1) - (6). The ranges of the numerical values of
the RD system quality indicators are given in Table 1.
Table 2
Ranges of numerical values of the DTS in RD system quality indicators
Level of quality Limitations
Min Мах
Bandwidth (B) 10 МB/s 100 МB/s
Effectiveness of information protection (ZΣ) 0.95 1.0
Structure risks (RΣ) 0.2 0.37
Data transmission error tolerance (σ) 0.5 byte 1.0 byte
The solution to the optimization problem of information parameters DTS in RD is to find the
maximum efficiency of the such system under certain states of its indicators.
In order to optimize the information parameters of the ship CTS RD system state the structure of
the software for the functioning of the RD system has been developed (Fig. 3).
Data transmission system capacity is determined by the maximum bandwidth of the DTS in RD
system, the receiving LAN, and the receiving DTS transport gateway.
The maximum throughput of a communication channel with additive noise is determined by
Shannon
S
B W log 2 (1 )
W N
(9)
DTS bandwidth objective function
(B) max(W , S / N )
(10)
As an example, Fig. 4 shows the results when solving the DTS bandwidth optimization problem
for W is channel bandwidth,
S / N 0 is signal-to-noise ratio at the destination receiver.
Figure 3: The structure of the DTS in RD software
Figure 4: The set of Pareto‐optimal solutions for the capacity B relative to the aggregate W and
S/N
The solution to the optimization problem of the information parameters of the DTS in RD system
based on the developed model allows us to find several Pareto-optimal solutions for the quality
indicator (criterion) - the DTS throughput, which affects the efficiency of the RD system.
5. Conclusion
In the formulation and solution of the optimization problem for the information parameters DTS in
RD of the CTS state, a set of independent parameters and conditions determining their admissible
values were determined, objective functions were obtained, and a method for solving optimization
problems was selected.
The developed model of optimization of information characteristics of the CTS state DTS in RD
system allows:
exclude the influence of the human factor when taking into account the CTS various
technological parameters;
generate reports on CTS states at any time for any reporting period for subsequent analysis;
monitor the state of CTS in real time, which will avoid accidents during their operation;
to reduce the risk of DTS in RD elements failures;
improve the DTS efficiency.
The solution to the optimization problem of information parameters DTS in RD on the basis of the
developed model makes it possible to find several Pareto-optimal solutions for indicators (criteria) of
quality that affect the DTS in RD system efficiency, the main of which are: the optimal number of
types of devices for controlling CTS technological parameters; topology efficiency; bandwidth;
speed; system error; the risk of component failures; efficiency of information protection.
6. References
[1] V.V. Vychuzhanin, N.D. Rudnichenko, Assessment of risks structurally and functionally
complex technical systems, Eastern-European Journal of Enterprise Technologies 2 (2014) 18–
22. doi:10.15587/1729-4061.2014.19846
[2] A.N. Dorokhov, V.A. Kernozhitskiy, A.N. Mironov, O.L. Shestopalova, Ensuring the reliability
of complex technical systems BHV, Saint Petersburg, 2011.
[3] V.Y. Tsvetkov, Complex technical systems, Educational resources and technologies 20 (2017)
86–92.
[4] V.V. Vychuzhanin, N.D. Rudnichenko, Information technology methods in diagnosing the state
of complex technical systems, Ecology, Odessa, 2019.
[5] V. Vychuzhanin, N. Shibaeva, Informatization of prognostication of risk structurally of the
difficult technical systems by means of models of the bayes networks of trust, Informatics and
mathematical methods in simulation 3 (2016) 205–214.
[6] V.A. Smirnov, Intelligent decision support system for the control of complex technical systems,
IOP Conf. Series: Journal of Physics 1327 (2019) 1–9.
[7] V. Freyman, I. Bezukladnikov, The application of soft decision making on decoding and
assessment of test diagnosing results within control systems elements, Proceedings of the 2017
XX IEEE International Conference on Soft Computing and Measurements, 2017, pp.124–128.
doi: 10.1109/SCM.2017.7970515
[8] V.V. Vychuzhanin, N.D. Rudnichenko, A.V. Vychuzhanin, A.Y. Kozlov, Information support of
risk management systems for automotive equipment failures, Informatics and mathematical
methods in modeling 3 (2019) 121–133.
[9] V.A. Fuks, Universal system for remote vehicle diagnostics, Young scientist 12 (2019) 40–44.
doi:10.1051/matecconf/201930201019
[10] V.D. Nogin, Multi-criteria decision making, BHV, Saint Petersburg, 2007.
[11] N. Vakhania, F. Werner, A brief look at multi-criteria problems - multi-threshold optimization
versus Pareto-Optimization. Multicriteria Optimization - Pareto-optimal and Related Principles,
IntechOpen, Zagreb (2020). doi:10.5772/intechopen.91169
[12] R. Steuer, Multiple Criteria Optimization: Theory, Computation, and Application, Wiley, New
York, NY, 1986.
[13] ISO 14830-1:2019 Condition monitoring and diagnostics of machine systems - Tribology-based
monitoring and diagnostics (2019).
[14] J. Horn, N. Nafpliotis, D. Goldberg, A niched pareto genetic algorithm for multiobjective
optimization, Proceedings of the 1st IEEE Congress of Evolutionary Computation, 1994, pp. 82–
87.
[15] M.A. Rosenman, J.S. Gero, Reducing the pareto optimal set in multicriteria optimization,
Engineering Optimization 8 (1985) 189–206.
[16] PC optimization program IOSO, 2020. URL: http://www.aec-service.ru/articles/10.html.