=Paper=
{{Paper
|id=Vol-3006/25_regular_paper
|storemode=property
|title=Development of a technology for collecting and analyzing data for monitoring based on an ontological approach
|pdfUrl=https://ceur-ws.org/Vol-3006/25_regular_paper.pdf
|volume=Vol-3006
|authors=Yuri I. Molorodov,Oleg V. Kasatkin
}}
==Development of a technology for collecting and analyzing data for monitoring based on an ontological approach==
https://ceur-ws.org/Vol-3006/25_regular_paper.pdf
Development of a technology for collecting and
analyzing data for monitoring based on an ontological
approach
Yuri I. Molorodov1,2 , Oleg V. Kasatkin2
1
Federal Research Center for Information and Computational Technologies, Novosibirsk, Russia
2
Novosibirsk State University, Novosibirsk, Russia
Abstract
One of the ways to build information models is ontological modeling. The use of ontologies greatly
facilitates the exchange of data between embedded models and utilities for the digital representation of
an object or a real-world system, sometimes called “digital twin” (DT). It is also important to establish a
correspondence between the DT, people and external programs. Based on the dictionary of the main
terms, classes, objects of the subject area and the relations between them, we have built an ontology of
the hydroelectric dam DT.
Keywords
Data collection and analysis system, monitoring of the technical condition of hydroelectric dams, dynamic
characteristics, processing of seismometric monitoring data, digital twin ontology of hydroelectric dams.
1. Introduction
A digital twin is a software analogue of a physical device that simulates internal processes,
technical characteristics and behavior of a real object under environmental influences. The
concept of digital twins implies the connection of the physical and digital world through the
interaction of information models. In other words, a mathematical model is created for a
physical object, a piece of equipment or an entire process, which is then used to analyze the
behavior of the object. Moreover, the digital model is constantly updated to fully correspond
to the current state of the real object. This makes it possible to identify unexpected changes
in processes, optimize the operating modes of equipment, prevent breakdowns and accidents,
which ultimately allows you to significantly increase the reliability and efficiency of operation.
According to regulatory documents, hydroelectric power plants are designed with a minimum
expectation for 100 years of operation, but with proper maintenance, the actual operation period
can be significantly increased and reach several hundred years [8]. The main equipment and
structures naturally wear out and require major repairs, modernization, replacement. The entire
life cycle of a hydroelectric power plant is a series of constant updates, upgrades, reconstructions
associated with the replacement of automation systems or with the replacement of main or
auxiliary equipment.
SDM-2021: All-Russian conference, August 24–27, 2021, Novosibirsk, Russia
" yumo@ict.sbras.ru (Y. I. Molorodov); o.kasatkin@g.nsu.ru (O. V. Kasatkin)
© 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
http://ceur-ws.org
ISSN 1613-0073 CEUR Workshop Proceedings (CEUR-WS.org)
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Yuri I. Molorodov et al. CEUR Workshop Proceedings 212–221
The use of a digital twin of the hydroelectric dam will significantly increase the efficiency of
operation, as well as determine the most accurate time of maintenance.
2. The use of digital twin
The digital twin uses information from sensors (sensors) installed on a real object not only to
update the current digital copy, but also to monitor the state and analyze the residual resource
of the object, predict defects.
To build a digital twin of the dam, first of all, it is necessary to build an information model.
One of the ways to build information models is ontological modeling. The use of ontologies
greatly facilitates the exchange of data between embedded models and utility programs of the
digital double, as well as between the digital double and people or external programs.
There are many definitions of ontology of varying degrees of formalization. Often, the
definition is given based on the methods of constructing an ontology. Nevertheless, despite
the abundance of approaches to the definition, the same composition of components is usually
used, which includes:
1) classes or concepts that are usually interpreted quite broadly and can include entities of any
kind;
2) instances, i.e. separate entities, the totality of which forms classes;
3) attributes — specific internal properties of classes and instances;
4) relationships mean the relationship between the classes of the subject area, for example, the
relation of taxonomy;
5) axioms, or rules of inference, which determine some always true statements that serve, for
example, to check information for correctness.
Figure 1: Ontological description of the digital twin of the dam.
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According to [6], the ontology is formally defined as follows:
𝑂𝑅 = ⟨𝐶, 𝑅, 𝐴, 𝑇, 𝐷⟩,
where 𝐶 = {𝐶1 , . . . , 𝐶𝑛 } — set of domain classes; 𝑅 = {𝑅1 , . . . , 𝑅𝑚 }, 𝑅𝑖 ⊆ 𝐶 × 𝐶 — the set
of relations defined on the classes of the subject area; 𝐴 = {𝑎1 , . . . , 𝑎𝑤 } — a set of attributes
describing the properties of concepts 𝐶 and relations 𝑅; 𝑇 is the set of standard attribute values;
𝐷 = {𝑑1 , . . . , 𝑑𝑛 } — the set of domains.
To build an ontology of the digital twin of a hydroelectric dam, we will use the CmapTools pro-
gram. Based on the works [2, 3, 12], as well as following the instructions of the methodological
manual [5], the ontologies presented in Figure 1 were constructed.
Figure 1 shows that the model of a digital twin of a hydroelectric dam requires constant
updating of simulation models and accumulation of data obtained from a real object to monitor
the current technical condition and predict the remaining resource. To solve these problems,
an information system for collecting and processing information is needed, which, along with
calculation and information models, can become a central link in the construction of a digital
twin of the dam.
3. Functionality and structure of the system
In the work [1], a method for assessing the technical condition of buildings and structures is
proposed, based on the analysis of changes in dynamic characteristics determined under the
influence of a microseismic background of a natural and man-made nature, i.e. without the
use of special excitation sources. These characteristics are manifested under dynamic loads
and reflect the technical condition of the building structure as a whole (frequencies and forms
of natural vibrations, attenuation decrements, statistical characteristics, etc.). The analysis of
trends in dynamic characteristics, taking into account the influence of seasonal environmental
factors, allows us to determine their irreversible changes and monitor the current technical
condition of the entire structure.
To perform the tasks of monitoring the state of the object in this work, the periodic (accord-
ing to a given schedule) registration of dam vibrations under the influence of microseismic
background and dynamic loads from the equipment operating at the HPP is selected.
Based on the analysis of regulatory documents, the following main functional requirements
for the system were identified:
1) the possibility of scheduled and periodic registration of microseismic vibrations of the dam;
2) the system must store records of seismometric monitoring data and information about the
functioning of the system;
3) the possibility of data analysis using various information processing algorithms, calculation
of the studied parameters and characteristics;
4) the ability to log error messages in the system, notify the user in case of exceeding the
permissible values of the observed values and problems in the system;
5) visualization of the analysis results.
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Figure 2: System functionality.
Figure 3: The basic structure of the system for collecting, storing, analyzing and visualizing data arrays.
In general, the operating procedure and functionality of the system under development are
shown in Figure 2.
The assessment of the technical condition of hydraulic structures is carried out in accordance
with the criteria adopted in the current building codes and rules for ensuring the safety of
operation of buildings and structures according to the theory of limit states. In the developed
system, the criteria for the safety of hydraulic structures in terms of seismometric monitoring
should be established in the form of functional dependencies between the dynamic characteristics
and parameters of external influences and loads. These dependencies are determined based on
the results of statistical analysis of data at the accumulation stage.
The structurally developed system should be a construction of related subsystems: data
collection, storage of the received data arrays, information processing and analysis, and data
visualization. Figure 3 shows the basic structure of the system for collecting, storing, analyzing
and visualizing data arrays.
The decomposition of the system into functional modules with well-defined data flows
between them can significantly reduce the complexity of the system and its development, as
well as increase the versatility of the modules.
3.1. Development of a data processing and analysis module
The data processing and analysis module was written in the C++ programming language in the
Microsoft Visual Studio 2019 development environment using the STL library.
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3.2. Input data format
As an input in this paper, we will consider the CIBF seismometric monitoring file format used in
the operation of the automated earthquake registration system and monitoring of the technical
condition of the dam (PAK-MZ) of the Krasnoyarsk HPP. This system periodically registers
stationary micro-vibrations of the dam at ten observation points in three directions of vibration
of the structure: longitudinal, transverse and vertical.
The CIBF file format is a specially developed format for seismometric monitoring data. The
file name includes the start time of data registration (the time of the first count) in the format
dd_mm_yyy_hh_mm_ss.cibf. All data is stored in the little-endian format (i.e. the lowest byte
comes first).
CIBF files consist of data packets (in one packet, data for all channels). Each packet contains
a header and data. The header includes the serial number of the packet for monitoring data
transmission, the parameters of the registration profile and the length of the data. Each packet
contains a 32-byte header and the data itself. The header structure is shown in Table 1.
The number of samples (the Sample Received field) shows how many samples are in the data
following the header. Thus, the headers can be used for iterating over the packages of the file.
The data is presented as a set of samples by channels. Each sample is represented as two
bytes of the data itself (the code with the ADC) and two bytes of service information (Table 2).
The bit fields F1, F2, and F3 are responsible for channel error, negative saturation, and positive
saturation, respectively. If the 8th bit of F1 is 0, then a channel error has occurred. If the 9th bit
of F2 is not equal to 0, then negative saturation has occurred. If the 10th bid of F3 is not equal
to 0, then a positive saturation has occurred. The lowest byte represents a cyclic code from the
range from 0 to 254.
Table 1
Package Header structure.
Byte
Field name Type Length Comment
number
0 Command word 2 1: test, 2: data, 3: service
2 SamplesResived word 2 Number of samples
4 DateTime TDateTime (double) 8 Start time
12 PackedSize integer 4 The size of the data packet after the
header
16 PackedNmb integer 4 Package number
20 SendDateTime TDateTime (double) 8 The time of sending — possibly an
empty field, a reserve
28 Reserved0 word 2 0
30 Reserved1 word 2 0
Table 2
Format of service information ctrl.
15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0
Ch a nn e l F3 F2 F1 Cy c li c c o de
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3.3. Data extraction and processing
To read CIBF format files, we will use the functionality of the fstream header file. Let’s create an
object of the std::ifstream class and link the class object to the CIBF file from which the reading
will be performed (hereinafter we will simply call it “file”). At the same time, we need to receive
data in binary form, so we will use the binary mode of opening the file. If it is impossible to
open the specified file, we will generate an exception of the std::exception type.
Recall that the smallest addressable element of the computer’s RAM is a byte. Let’s calculate
the exact length of the file. To do this, use the seekg function to move the file pointer to the
final position and use the tellg function to find out its number (in bytes), then use the seekg
function again to return the pointer to the starting position for further work.
Note that the computer’s central processor uses a machine word as the main unit of work with
memory, the size of which depends on the processor architecture. When storing an arbitrary
object in memory, it may happen that some field consisting of several bytes will cross the
“natural boundary” of machine words in memory. Some processor models cannot access such
data in memory, or they take longer to access data located inside an entire machine word in
memory. Therefore, some C++ compilers can use automatic “data alignment”. This optimization
consists in inserting insignificant bytes between the fields of the data structure when placing it
in memory to speed up access to this structure.
For the convenience of reading, we will describe data packages using structures — the custom
data type struct of the C++ language. Each packet will be a structure, whose members will be
other structures — the packet header and an array of samples. When describing structures, we
will use the pragma pack(push,1) and pragma pack(pop) preprocessor directives to exclude any
data alignment.
Create an integer variable with a zero initial value to track the number of the current position
(in bytes) from the beginning of the file. Further, everywhere under the word “reading” we will
mean byte-by-byte extraction of the structure into a variable of the corresponding type using
the read operator. The process of reading data packets will occur in the body of the main while
loop, the condition for the completion of which will be reaching or exceeding the number of
the current position of the file length value. The increment of the value of the current position
number will occur after using the read operator.
In the body of the main loop, we will first read the packet header. At the beginning of the
header of each package is a number that characterizes the contents of the package. We will use
it to control the reading. If the number 2 is the first in the read packet, then we determine how
many samples are in the packet and read them into the buffer. Otherwise, we notify the user
that this package contains service or test information.
In the cycle, we will process the received data according to the number of read samples. First
of all, from the ctrl field, we will determine the number of the channel on which the countdown
was received. Next, it is necessary to convert the value 𝑁 obtained from the ADC to the form
20𝑁 1 1
𝐴= 16
· · ,
2 𝐾 𝐾𝐸𝑀 𝑆
where 𝐴 is the acceleration amplitude; 𝐾 is the total signal gain; 𝐾𝐸𝑀 𝑆 is the electrome-
chanical coupling coefficient. The 𝐾𝐸𝑀 𝑆 of the sensor determines its sensitivity. At the
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Table 3
Gain factors 𝐾.
Pavilion, mark, section, component Gain
1k PV.1, mark.223, sec.Ba, Z 100 × 4 = 400
2k PV.1, mark.223, sec.Ba, Y 100 × 4 = 400
3k PV.1, mark.223, sec.Ba, X 100 × 4 = 400
4k PV.2, mark.223, sec.8, Z 100 × 2 = 200
5k PV.2, mark.223, sec.8, Y 100 × 2 = 200
6k PV.2, mark.223, sec.8, X 100 × 1 = 100
7k PV.3, mark.132, sec.8, Z 100 × 4 = 400
8k PV.3, mark.132, sec.8, Y 100 × 4 = 400
9k PV.3, mark.132, sec.8, X 100 × 4 = 400
10k PV.4, mark.223, sec.22, Z 100 × 1 = 100
11k PV.4, mark.223, sec.22, Y 100 × 1 = 100
12k PV.4, mark.223, sec.22, X 100 × 1 = 100
13k PV.5, mark.132, sec.22, Z 100 × 4 = 400
14k PV.5, mark.132, sec.22, Y 100 × 4 = 400
15k PV.5, mark.132, sec.22, X 100 × 4 = 400
16k Reserved
17k PV.6, mark.244, sec.37, Z 100 × 1 = 100
18k PV.6, mark.244, sec.37, Y 100 × 1 = 100
19k PV.6, mark.244, sec.37, X 10 × 4 = 40
20k PV.7, mark.132, sec.37, Z 100 × 2 = 200
21k PV.7, mark.132, sec.37, Y 100 × 2 = 200
22k PV.7, mark.132, sec.37, X 100 × 2 = 200
23k PV.8, mark.223, sec.54, Z 100 × 1 = 100
24k PV.8, mark.223, sec.54, Y 100 × 1 = 100
25k PV.8, mark.223, sec.54, X 10 × 4 = 40
26k PV.9, mark.132, sec.54, Z 100 × 2 = 2000
27k PV.9, mark.132, sec.54, Y 100 × 2 = 200
28k PV.9, mark.132, sec.54, X 100 × 2 = 200
29k PV.10, mark.223, sec.71, Z 100 × 4 = 400
30k PV.10, mark.223, sec.71, Y 100 × 4 = 400
31k PV.10, mark.223, sec.71, X 100 × 4 = 400
32k Reserved
Krasnoyarsk HPP dam, 𝐾𝐸𝑀 𝑆 = 2𝑉 sec2 /m. The total signal gain is calculated as the product
of the pre-gain coefficients 𝐾1 and the main gain 𝐾2. The values of the gain coefficients 𝐾 are
shown in Table 3.
The obtained acceleration amplitudes are distributed over channels using objects of the std
container class::vector.
3.4. Presentation and analysis of the results of the module
The implemented module was tested on CIBF files. The correctness of the module operation and
the results obtained was confirmed by a specialist of the PAK-MZ system of the Krasnoyarsk
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HES.
Based on the obtained values of the acceleration amplitudes, the statistical characteristics of
the oscillations are calculated. An example of the calculated statistical characteristics for one of
the monitoring sessions is shown in Table 4.
One of the methods for visualizing the process of changing the amplitudes of accelerations
over time is the construction of seismic tracks. A seismic trace is a graph of the dependence of
the signal level of seismic waves or noise on the time of their registration.
Let’s use the Gnuplot charting program. To build a seismic track, it is necessary to know the
duration of the registration of vibrations (512 seconds) and the number of recorded samples
(the length of the object std::vector, where the data is stored after extraction). If we set aside the
time in seconds horizontally, and the values of the acceleration amplitudes (mm/s2 ) vertically,
Table 4
Statistics for the session 03/04/2011/19:00.
Channel No. Coordinate Average, mm/s2 Variance, mm2 /s4 Frequency, Hz
1 Z −0.02 0.01 4.63
2 Y −0.01 0.01 4.87
3 X −0.02 0.04 4.33
4 Z −0.04 0.16 4.73
5 Y −0.03 0.09 5.81
6 X −0.08 4.84 4.32
7 Z −0.02 0.01 4.5
8 Y −0.01 0.01 4.89
9 X −0.02 0.04 4.12
10 Z −0.09 0.11 4.71
11 Y −0.07 0.1 5.2
12 X −0.06 0.63 4.31
13 Z −0.02 0.04 4.53
14 Y −0.02 0.02 4.03
15 X −0.02 0.14 4.36
17 Z −0.08 0.12 4.72
18 Y −0.06 0.13 6.02
19 X −0.12 3.9 4.38
20 Z −0.04 0.09 4.22
21 Y −0.03 0.07 5.11
22 X −0.03 0.12 4.6
23 Z −0.09 0.12 6.17
24 Y −0.09 0.13 4.97
25 X −0.2 3.24 4.53
26 Z −0.03 0.05 4.81
27 Y 0.07 0.03 5.54
28 X −0.05 0.1 4.34
29 Z −0.02 0.01 7.31
30 Y −0.03 0.02 5.98
31 X −0.02 0.04 4.87
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Figure 4: Seismic route of the 2nd channel of the session 03/04/2011/07:00.
then we will get a visualization of the dam oscillation process in the specified observation point
and direction in time on the graph. An example of a seismic route is shown in Figure 4.
4. Conclusion
On the basis of regulatory documents regulating the maintenance of hydroelectric dams, the
subject area is investigated and an information system for collecting and analyzing seismometric
monitoring data is developed. The basis for designing an information system is an ontological
approach. Using this approach, the ontology of the digital twin of the hydroelectric dam is
constructed. The main functional capabilities of the information system are formulated and
its structure is described. The data processing and analysis module is implemented in the C++
programming language. The implemented module has been tested on CIBF-format seismometric
monitoring data packages.
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