=Paper=
{{Paper
|id=Vol-3309/short15
|storemode=property
|title=Information software of multi-level systems of monitoring and diagnostics of complex technical objects
|pdfUrl=https://ceur-ws.org/Vol-3309/short15.pdf
|volume=Vol-3309
|authors=Nadiia Marchenko,Hanna Martyniuk,Olena Monchenko,Larysa Chubko,Tetiana Scherbak
|dblpUrl=https://dblp.org/rec/conf/ittap/MarchenkoMMCS22
}}
==Information software of multi-level systems of monitoring and diagnostics of complex technical objects==
https://ceur-ws.org/Vol-3309/short15.pdf
Information software of multi-level systems of monitoring and
diagnostics of complex technical objects
Hanna Martyniuka, Nadiia Marchenkob, Olena Monchenkob, Larysa Chubkob and Tetiana
Scherbakb
a
Mariupol State University, Povitroflotsky ave. 31, Kyiv, 03037, Ukraine
b
National Aviation University, Liubomura Huzara ave.1, Kyiv, 03058, Ukraine
Abstract
The article is considered intellectual information systems for monitoring and diagnosing
complex technical objects, considering modern information technologies. Computer
intellectualization ways of the functioning modes of such complex technical objects are shown.
The analysis of efficiency parameters is carried out and made it possible to determine the main
task decomposition of developing methods and means of building adaptive systems of multi-
level monitoring and diagnosis of complex technical objects. The peculiarity of the work is to
solve the building systems complex task of multi-level monitoring and diagnosing complex
technical objects as integrated systems based on the self-organization principles of complex
systems. The proposed two-level system for monitoring and diagnosing the condition of
complex industrial facilities, which differs from analogues in the ability to automatically select
the optimal operating modes of subsystems when the characteristics of the incoming flow of
applications change, is a single approach that allows synthesizing the optimal structure of
multi-level monitoring and diagnosis systems at the design stage and choosing the optimal
operating mode of subsystems during operation. The research method of multi-level
monitoring and diagnosis systems based on the multi-level model of mass service using the
block of adaptation to changes in the intensity of the incoming flow is presented. The work
presents the development of a generalized criterion for assessing the effectiveness of research
into systems of multi-level monitoring and diagnosis of complex technical objects and partial
criteria for each subsystem, as well as research and analysis of methods and means of
organizing adaptive systems of multi-level monitoring and diagnosis of complex technical
objects and the selection of effective modes work.
Keywords 1 Automated control systems, multi-level monitoring and diagnostics systems,
adaptive multi-level monitoring and diagnostics systems, assessment of effectiveness, multi-
level mass service models.
1. Introduction
Modern atomic, thermal and hydroelectric power plants, chemical and metallurgical productions,
and large production enterprises are complex technical objects (CTO) operating under conditions of
significant wear and tear of the main and auxiliary equipment. In the conditions of slow modernization,
the only possibility of maintaining the operational efficiency of equipment is the development and
application of object monitoring systems for the purpose of the timely and comprehensive analysis of
ITTAP’2022: 2nd International Workshop on Information Technologies: Theoretical and Applied Problems, November 22–24, 2022,
Ternopil, Ukraine
EMAIL: ganna.martyniuk@gmail.com (A. 1), nadiiamar@gmail.com (A. 2); monchenko_olena@ukr.net (A. 3), chubkolarysssa@gmail.com
(A. 4); tais2004@i.ua (A. 5).
ORCID: 0000-0003-4234-025X (A. 1); 0000-0001-5008-4116 (A. 2); 0000-0002-8248-5704 (A. 3); 0000-0003-4647-3156 (A. 4); 0000-
0003-1170-8154 (A. 5).
©️ 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)
technological processes taking place, diagnosis of the condition, determination of the residual resource
and forecasts of future behavior [1, 2].
Monitoring systems used in industry, built on the basis of classical information and measurement
systems, have some drawbacks [3]. They are designed for enterprises of a certain profile, are highly
specialized, and can work only with a specific object, they are subject to the influence of external
destabilizing factors that lead to failures and malfunction; poorly adapted to changes in the processes
in the object, do not take into account the influence of the human factor; they are poorly protected
against unauthorized, intentional or accidental interference with their work.
Systems of multi-level monitoring and diagnostics (SMMD) are partially devoid of the mentioned
shortcomings. Compared to classical information and measurement systems, SMMD have increased
resistance to external factors, increased security, and ensure the security of data transmission [1, 2]. The
application of this concept (including in multi-level information and measurement systems (IMS) of
monitoring and diagnostics) assumes that maintenance and repair of CTO should be carried out
according to the actual condition [4]. That’s why a much larger part of the equipment must be covered
by reliability assurance systems, which must carry out constant or periodic control of its actual technical
condition.
According to well-known sources, the tasks listed above sometimes are combined under the general
name “Asset Management” [5]. Currently, both engineering and scientific work in this field is actively
being conducted, leading manufacturers of powerful electrical equipment and it has already offered a
number of software products designed to collect and summarize statistical information about operating
conditions and the actual condition of CTO equipment.
Numerous publications aimed at solving the above-mentioned problems, we could point out works
[5, 6, 7, 8], each of which considers certain issues related to the use of monitoring and diagnostic
systems.
Thus, the work [6] is devoted to the issues of preliminary preparation of experimental data before
their further processing by computing means, in particular, with the help of IMS monitoring and
diagnostics. Moreover, data preparation has performed according to certain algorithms presented in this
work and has provided an opportunity to reduce their volume for further processing. The work [7] is
devoted to the issues of ensuring two-way exchange of information between different levels of electric
power facilities. The paper presents the results of a complex experimental study with the statistical
characteristics determination of information exchange wireless channel between objects at a power
substation with a voltage of 500 kV. The paper [8] is considered the application of monitoring the state
methods of individual power transformer units based on the use of informational diagnostic signals.
In recent years, the main directions of scientific research of the SMMD are methods of obtaining,
transmitting, storing, monitoring and diagnosing information, improving the methods of processing the
received data. The starting points for building models of objects are the results of measurements of their
parameters, as well as data about the environment [9, 10]. Objects have a multi-level structure that are
changing over time. Their state and behavior are typically described in discrete time and discrete state
space.
The methods of constructing models of monitoring and diagnostic objects (MDO) that are
currently used, in many respects, do not meet the requirements of practice. Due to the complexity of
the tasks to be solved, their construction requires significant time costs, as well as costs of human and
other resources. In addition, the emerging MDO models are not always accurate and reliable. Thus, the
problem of building MDO models and their application to solving applied problems is relevant [1, 2,
11].
The integration of multi-level monitoring and diagnostics necessitated the creation of methods
and means of building the SMMD. At the present stage, the problem of developing methodological
principles for the construction of automated SMMD for the class of complex industrial facilities (HPP,
TPS, NPP) is becoming particularly relevant [12, 13].
Certain difficulties arise in the event of the need to rebuild the structure of the monitoring system,
change the algorithm of its work, solve scaling tasks, and process large data flows. Therefore, the
development and research of SMMD, combining the ability to work reliably in harsh operating
conditions with flexibility of application and competitive cost, is a serious scientific problem, and the
solution of which is of great importance for domestic science and technology [1,7]. The use of work
results increases the safety of operation of CTO, which is a significant contribution to the development
of the economy of our country. Based on the above, the research topic is relevant.
The purpose of the work is the development and research of SMMD, which provide effective and
high-quality monitoring of the parameters of complex technical objects with the simultaneous saving
of material resources for the design, implementation, and operation of CTO.
To achieve the goal, the following scientific tasks were solved:
- to analyze the subject area of building models of observed objects based on the data of multi-level
monitoring and diagnostics of their conditions. To formulate a scientific problem, to determine the
requirements for multilevel models and methods of their synthesis.
- formulate construction principles, develop a structural diagram and a mathematical model of the
SMMD;
- to develop and research mathematical models of the main functional components of SMMD, based
on the proposed models to develop methods for determining system parameters and algorithms for
synthesizing their optimal characteristics;
- justify the expediency of using intelligent information processing methods in SMMD, develop
decision-making models and knowledge presentation, offer hardware and software tools for the
implementation of intellectual systems; analyze the method of choosing the channels number, and
develop algorithms for optimizing the multilevel signal conversion function, which, due to their
versatility, can be used in related fields of science and technology.
2. Development of theoretical and practical principles and mathematical
models for the study of multi-level monitoring and diagnostics systems
In the process of analysis, it was found that complex technical objects have a number of features:
multifacetedness and uncertainty of behavior, hierarchical structure, excess and variety of constituent
objects of elements and subsystems, the ambiguity of connections between them, multivariate
implementation of management functions, territorial distribution. Therefore, in modern conditions, the
development of methods, algorithms, and technical means of state constant monitoring of a complex
object, analysis of the processes taking place in it, diagnosis, and prediction of the object’s behavior in
the future is becoming very relevant. The most effective multi-level monitoring tool is information and
measurement systems, mathematical models, the theory of choice, and decision-making [2, 8].
The main problems in the intellectualization of SMMD are the formation and selection of the
researched object model; selection of measurement and control methods; parameters selection of the
measured object; system efficiency assessment.
The selection of existing methods, the method of logical inference based on the application of the
theory of fuzzy sets, and the solution of the optimization problem were considered as a decision-making
strategy.
2.1. Development of a mathematical model of multi-level monitoring and
diagnostics systems
To assess the possibility of restoring multidimensional signals, a generalized mathematical model
of SMMD was developed, which is determined by the equations [4, 10]:
𝑦1 (𝑥1 , 𝑡) = 𝐹1 (𝑥1 , 𝑥2 , … , 𝑥𝑛 , 𝑎1 , 𝑎2 , … , 𝑎𝑚 , 𝑡, 𝑧)
𝑦 (𝑥 , 𝑡) = 𝐹2 (𝑥1 , 𝑥2 , … , 𝑥𝑛 , 𝑎1 , 𝑎2 , … , 𝑎𝑚 , 𝑡, 𝑧) (1)
{ 2 2 ,
……………………………………………………
𝑦𝑛 (𝑥𝑛 , 𝑡) = 𝐹𝑛 (𝑥1 , 𝑥2 , … , 𝑥𝑛 , 𝑎1 , 𝑎2 , … , 𝑎𝑚 , 𝑡, 𝑧)
𝑦 – output signals of SMMD; 𝐹 is a set of operators; 𝑥 – input processes; 𝑎 - internal parameters; 𝑡 -
time; 𝑧 is the influence of the environment.
If the internal parameters 𝑎1 , 𝑎2 , … , 𝑎𝑚 are constant, the model can be considered dynamic
deterministic and represented using Liapunov-Likhtenshtein operator [10]:
∞
𝑡 𝑡
𝑦1 (𝑥1 , 𝑡) = 𝐹𝐿𝐿 [𝑥1 (𝑡)] = ∑ ∫ … ∫ 𝑘𝑖 (𝑡, 𝜏1, 𝜏2 , … , 𝜏𝑖 )𝑥1 (𝜏1 ), 𝑥1 (𝜏2 ), … , 𝑥1 (𝜏𝑖 )𝑑𝜏1 𝑑𝜏2 … 𝑑𝜏𝑖
𝑖=0 0 0
∞
𝑡 𝑡
𝑦2 (𝑥2 , 𝑡) = 𝐹𝐿𝐿 [𝑥2 (𝑡)] = ∑ ∫ … ∫ 𝑘𝑖 (𝑡, 𝜏1, 𝜏2 , … , 𝜏𝑖 )𝑥2 (𝜏1 ), 𝑥2 (𝜏2 ), … , 𝑥2 (𝜏𝑖 )𝑑𝜏1 𝑑𝜏2 … 𝑑𝜏𝑖 (2)
𝑖=0 0 0
……………………………………………………
∞
𝑡 𝑡
𝑦𝑛 (𝑥𝑛 , 𝑡) = 𝐹𝐿𝐿 [𝑥𝑛 (𝑡)] = ∑ ∫ … ∫ 𝑘𝑖 (𝑡, 𝜏1, 𝜏2 , … , 𝜏𝑖 )𝑥𝑛 (𝜏1 ), 𝑥𝑛 (𝜏2 ), … , 𝑥𝑛 (𝜏𝑖 )𝑑𝜏1 𝑑𝜏2 … 𝑑𝜏𝑖
{ 𝑖=0 0 0
𝑘𝑖 is the feature of the message 𝑥𝑖 in the multidimensional channel.
Replacing the Liapunov-Likhtenshtein operator with the Volterr series makes it possible to assess
the degree of model adequacy of the problems nature to be solved, to analyze the informational,
energetic, and metrological characteristics of SMMD. External influences are compensated by system
internal parameters:
𝑦1 (𝑥1 , 𝑡) = 𝐹𝜔 (𝑥1 (𝑡, 𝑧) − 𝑦1 (𝑡, 𝑎1 , 𝑎2 , … , 𝑎𝑚 ))
𝑦2 (𝑥2 , 𝑡) = 𝐹𝜔 (𝑥2 (𝑡, 𝑧) − 𝑦2 (𝑡, 𝑎1 , 𝑎2 , … , 𝑎𝑚 ))
(3)
……………………………………………………
{ 𝑛 (𝑥𝑛 , 𝑡) = 𝐹𝜔 (𝑥𝑛 (𝑡, 𝑧) − 𝑦𝑛 (𝑡, 𝑎1 , 𝑎2 , … , 𝑎𝑚 ))
𝑦
2.2. Characteristics of the SMMD model
As a result of the system structure analysis, information flows and time modes of operation, the
SMMD under investigation can be presented in the form of a single-channel multiphase mass service
system (MSS) with Puason’s input and output flows, exponential service time of requests in each phase
and thinning of the input flow by the first and second phases [1, 11, 12]. The structural diagram of the
MSS is presented in fig. 1.
Figure 1: Structural diagram of the mass service system model
The MSS contains N objects of technogenic systems (O), a subsystem of control (M), diagnostics
(D), forecasting of the residual resource (P), decision-making (PR) and adaptation (A). The input flow
of applications 𝜆𝑀𝐼 is first received at the input of subsystem M and is further processed in subsystems
D, P and PR. Flows 𝜆𝐹1 and 𝜆𝐹2 characterize flows of screened applications corresponding to regulated
states of complex technical objects.
The work uses the method of researching multiphase systems as a set of serially connected
autonomous MSSs, united by joint control with the help of an adaptation subsystem [4, 10]. When
creating typical MSSs, the choice of performance is made for the most stressful mode of the system at
𝜆𝑀𝐼 = 𝜆𝑚𝑎𝑥 , taking into account the fact that in other cases the system will be guaranteed to work
stably. At the same time, the redundancy that occurs during the operation of the system is usually not
used [4].
A positive feature of the considered SMMD is that, when𝜆𝑀𝐼 < 𝜆𝑚𝑎𝑥 , the adaptation subsystem
A initiates an increase in the service time 𝑇0 for each subsystem and ensures the stability of the load
factor of the processing nodes 𝜌𝑤 according to the following expressions:
𝜌 = 𝑓{𝜆 ∈ 𝑉𝐼 }; (4)
𝜌
𝜌𝑤 = 𝜌 ≤ 1: {𝑇𝑠𝑦𝑠 ≤ 𝑇𝑠𝑦𝑠 𝑚𝑎𝑥 , 𝑇0 = 𝜆 𝑤 : 𝐷(𝑇0 ) ≥ 𝐷𝑠 };
𝑚𝑎𝑥
𝜌
𝑇𝑂𝐼 = 𝜆𝑤 : 𝜆𝑖−1 ≤ 𝜆 ≤ 𝜆𝑖 , 𝑖 = 1, … , 𝑛;
𝑖
𝑇𝑠𝑦𝑠 is the time of finding the application in the subsystem, 𝐷𝑠 is the minimum permissible reliability
of decision-making; 𝜆𝑖 is the current intensity of requests at the input of the subsystem, which is
determined on the basis of forecast data and flow thinning coefficients; 𝑛 is the number of application
intensity levels.
Value 𝑇𝑂𝐼 is a setting parameter for each subsystem as a reaction to a change in λ, and in accordance
with which each subsystem automatically rebuilds its functional model taking into account the
maximum reliability of decision-making, according to the expression:
𝑥𝑖𝑗 ∈ 𝑋𝑖 : 𝐷𝑖 = 𝑚𝑎𝑥𝐷𝑖 {𝑉2 , 𝑇𝑂𝐼 }, 𝑖 = 1,2,3,4; 𝑗 = 1,2, … (5)
The selection of SMMD parameters according to (4), (5) allows to implement the algorithm of two-
level adaptation of the system. At the first level, the system characteristics 𝜌 і 𝑇𝑂𝐼 corresponding to the
𝑉1 parameters are selected, at the second level, the subsystem characteristics corresponding to the 𝑇𝑂𝐼
and 𝑉2 parameters are selected.
So, the work defines a class of complex technical objects, the main feature of which is the difficulty
for monitoring and diagnosis, due to the stochasticity of the processes, the complexity of the design,
and the lack of information available for control. The need to measure and control the parameters of
complex technical objects and analyze the received data in real time is substantiated. The main and
most effective tool for monitoring CTO is the information and measurement system, the characteristics
of which largely determine the quality of multi-level monitoring and diagnostics. It has been proven
that the most promising type of information and measurement systems for solving the existing problems
of multi-level monitoring and diagnostics are SMMD, which provide effective, flexible and constant
control of CTO parameters under conditions of destabilizing influence of external factors.
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