=Paper=
{{Paper
|id=Vol-3790/paper26
|storemode=property
|title=Intelligent analysis of the causes of the Challenger space shuttle disaster
|pdfUrl=https://ceur-ws.org/Vol-3790/paper26.pdf
|volume=Vol-3790
|authors=Igor Petrov,Vladislav Mykhailenko,Roman Kharchenko,Yurii Gunchenko,Aleksandr Kochetkov,Oksana Zui
|dblpUrl=https://dblp.org/rec/conf/icst2/PetrovMKGKZ24
}}
==Intelligent analysis of the causes of the Challenger space shuttle disaster==
https://ceur-ws.org/Vol-3790/paper26.pdf
Intelligent analysis of the causes of the Challenger space
shuttle disaster
Igor Petrov4 , Vladislav Mykhailenko1, Roman Kharchenko1, Yurii Gunchenko2, Aleksandr
Kochetkov3 and Oksana Zui2
1
National University "Odessa Maritime Academy", Didrikhson str.8, Odessa, 65029, Ukraine
2
I.I. Mechnikov National University, Dvorianska Str., 2, Odessa, 65025, Ukraine
3
Odessa National Maritime University, Mechnikova str.8, Odessa, 65029 Ukraine
4
National University "Odessa Maritime Academy", Didrichson street 8, Odessa, 65029, Ukraine
Abstract
The publication presents an intelligent analysis of data indicating the causes of the Challenger space
shuttle disaster. Data analysis was performed using a specialized Orange software package. Statistical
indicators of failure of the fuel tank sealing rings, as well as the corresponding air temperature on the day
of the spacecraft launch, were used as initial data. Data analysis using logistic regression indicates that at
a temperature of 36 degrees Celsius there is an 89% chance of O-ring failure, which indicates a significant
risk of defects. Overall, the average probability of failure for all five rings of the spacecraft was 56%,
which confirmed the conclusions of the investigative commission about the causes of the disaster. The
research methods used were approaches of regression analysis of data and the method of clustering data
K average. The degree of adequacy of the mathematical model implementing the method of logistic
regression was 74%, which indicates an acceptable result of the obtained data. The proposed model allows
predicting with a high degree of accuracy the possible failure of fuel tank elements of modern spacecraft.
Keywords
Intelligent Analysis, Space Shuttle Challenger, O-Ring, Logistic Regression, k-means method
1. Introduction
The prototype of the Starship spacecraft, developed by Elon Musk's SpaceX for flights to Mars and
the Moon, landed for the first time during flight tests on March 3. As soon as the applause died
down - it was the first successful landing - the device exploded. Questions for Musk only increased:
why continue tests that always end in an explosion on the ground or burning up in the air?
Meanwhile, another Starship prototype is scheduled to launch in the coming days, and on March
18, a critical test of its main competitor, the super-heavy rocket Space Launch System of Boeing,
was carried out. After several decades, when super-heavy rockets were not developed because
there were no suitable tasks for them, they are in demand again - a new technological competition
for the creation of space rockets has begun. The start of Starship testing can be counted from the
first successful flights of the very first demonstrator with a single engine, Starhopper: on April 5,
2019, it jumped one meter, then made two more jumps of 20 and 150 meters, and now decorates the
test site, forming a backdrop for testing new rocket prototypes.
Their fate turned out to be more difficult. The next Starship prototypes Mk1 (the same one
against the backdrop of which Elon Musk held his historic press conference on September 28,
2019), SN1 and SN3 exploded during pressure tests of the tanks. Each such accident led to a change
in the design of the rocket and launch facilities.
Already in August and September 2020, the flight demonstrators SN5 and SN6 made their first
successful ascents on one engine to 150 meters and landed softly without any emergency
situations. On February 2, 2021, the next flight demonstrator, SN9, was launched. It routinely
ascended to an altitude of 10 kilometers and descended from it, but during the landing approach
itself, one of the two required engines did not turn on. As a result, the prototype crashed into the
ground again with a powerful explosion[1].
_______________________
1
ICST-2024: Information Control Systems & Technologies, September , 23 25, 2024, Odesa, Ukraine
firmn@gmail.com (I. Petrov); vlamihailenod@gmail.com (V. Mykhailenko); romannn30@gmail.com (R. Kharchenko);
gunchenko@onu.edu.ua (Y. Gunchenko); 0679016767@ukr.net (A. Kochetkov); oks.zuj@gmail.com (O. Zui)
0000-0002-8740-6198 (I. Petrov); 0000-0003-2793-8966 (V. Mykhailenko); 0000-0003-3051-7513 (R. Kharchenko); 0000-
0003-4423-8267 (Y. Gunchenko); 0000-0001-8200-4897 (A. Kochetkov); 0000-0001-9520-4441 (O. Zui)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
Thus, it can be stated that the study of the causes of spacecraft accidents is a relevant task. And
to understand the cause-and-effect relationships of the accidents that have occurred, it is necessary,
according to the authors, to analyze in detail the history of unsuccessful spacecraft launches using
the Shuttle as an example.
The Challenger disaster occurred on January 28, 1986, when the shuttle was destroyed at the
very beginning of the mission as a result of a fuel tank explosion 73 seconds into the flight, which
led to the death of all 7 crew members (Fig.1).
The disaster occurred over the Atlantic Ocean off the coast of the central part of the Florida
Peninsula, USA. According to the conclusions of the commission to investigate the causes of the
disaster, the destruction of the spacecraft was caused by damage to the sealing ring of the right
solid rocket booster during launch. Damage to the ring caused a hole to burn out in the side of the
accelerator, from which a jet stream flowed towards the external fuel tank. This led to the
destruction of the tail mount of the right solid rocket booster and the supporting structures of the
external fuel tank. Elements of the complex began to shift relative to each other. The destruction of
the external fuel tank led to the ignition of fuel components and the explosion of the ship [2].
Figure 1: Photo of the Shuttle Challenger before launch [1]
Taking into account the global nature of the disaster, the task of intellectual analysis of data on
the cause-and-effect factors that led to the disaster is of scientific interest. It is known that Data
mining (Russian: data mining, data mining, in-depth data analysis) is a collective name used to
denote a set of methods for detecting previously unknown, non-trivial, practically useful and
accessible interpretation of knowledge in data necessary for decision-making in various fields
human activity. Data mining methods can be used both for working with large data and for
processing relatively small amounts of data (obtained, for example, from the results of individual
experiments on the example of the failure of the sealing rings of a spacecraft [2].
2. Analysis of the design features of the spacecraft
To launch each shuttle, two solid fuel accelerators were used, consisting of seven sections, six of
which were connected in pairs at the manufacturing stage. The resulting four parts were assembled
together at the cosmodrome of the Space Center John F. Kennedy in the Vertical Assembly
Building. The factory connections of the sections were covered with an asbestos-silicate coating,
and the connections made at the cosmodrome were closed with two rubber sealing rings (based on
the results of the investigation into the causes of the disaster, the number of rings was increased to
three) [2]. The coating was required in order to prevent the breakthrough of high-temperature
gases and ensure normal operation of the accelerator during the entire acceleration stage.
Engineers at the Marshall Space Center pointed out that the proposed connection of parts of the
Thiokol accelerator was unacceptable. One of the engineers suggested that the electrical
connections made using a second O-ring completely useless, but solid rocket booster project
manager George Hardy did not attach much importance to this and did not submit the reports to
the engineers for approval. As a result, connecting parts using O-rings was approved for flights in
1980 [2]. Even before the disaster, it was determined that the seals were subjected to significantly
more stress after increasing pressure, resulting in O-ring failure after assembly (from 50 to 100 and
then to 200 psi). The reason for the burning of the rings was the resulting holes in the putty,
through which, after launch, concentrated gas flows appeared, destroying the rings. Even though it
is known that holes in the putty cause erosion, and that higher leak flow pressure causes these
holes to enlarge, the Thiokol expands and NASA decided to apply increased pressure to ensure that
the ring joint actually passed the test for tightness [2]. Thiokol provides complete information
thanks to the effectiveness of the O-rings depending on the initial temperature of the assembly. Its
engineers found that at 100°F the O-ring maintained a seal, at 75°F the seal failed in 2.4 seconds,
and at 50°F the O-ring was completely ineffective. The peculiarity was that during thermal
elongation of a working accelerator ring, it is necessary to take into account the diameter angle
following the diameter of the connection, and when cold enough, the rings became insufficiently
elastic for this. However, due to pressure from their management, who were afraid of transferring
the contract for the supply of accelerators to another company, the engineers were not able to
convey all serious problems to NASA. Figure 2 shows a diagram of the connection between two
sections of the shuttle solid rocket booster: A - 12.7 mm thick steel wall, B - second o-ring, C -
backup o-ring, D - stability, E - accelerator section, F - thermal insulation , G - coating, H - sealing
paste, I - rocket fuel. The night of January 27-28 was unusually cold by Florida standards.
Temperatures in the Cape Canaveral area dropped to -1°C. Some of the specialists sounded the
alarm. During one of the previous launches, which took place after the shuttle sat on the launch
pad during a cold night, particularly severe damage to the joint was noted. It was suggested that
low temperatures reduced the flexibility of the vulcanized sealing material, making it more brittle
and vulnerable to stress. It is known that on the morning of January 28, several Thiokol engineers
demanded the cancellation of the upcoming launch.
Figure 2: Connection diagram of two sections of the shuttle solid propellant accelerator
But, not wanting to disrupt the planned flight schedule, NASA officials opposed it. As for the
Challenger crew, they did not even know about the existence of any problems with the boosters.
After Challenger lifted off from the launch pad, the ship was doomed. Soon a stream of hot gases
made its way through the sealing ring that had lost its elasticity and began to hit the fuel tank. She
punched a hole in it and also burned out the connection to the accelerator itself. As a result, the
accelerator simply came off and hit the fuel tank, which led to its instant destruction [3]. Based on
O-ring observations, it is known that 24 flights of the spacecraft were successful. Also, the
engineers calculated that a disaster could occur if all five main rings were broken, i.e. their seal will
be compromised. At the same time, according to statistics, there were flights in which there was
not a single breakage of the rings, but there were flights in which from one to three rings broke.
Also, all previous flights of the ship before the disaster were at higher temperatures. On the day of
the disaster, the air temperature was 36 degrees Fahrenheit and analysis of rings at this
temperature had not previously been carried out. Based on the importance of analyzing the main
causes of the disaster and preventing such events in the future, the scientific and practical task of
analyzing the creation of a system for predicting the probability of aircraft accidents is relevant. To
solve the problem, data mining methods (DMA) were used [4]. IAD or (Data Mining) is a decision
support process based on searching for hidden patterns (information patterns) in data. In this case,
the accumulated information is automatically generalized to information that can be characterized
as knowledge. In general, the IAD process consists of three stages [5]: identifying patterns (free
search); using identified patterns to predict unknown values (predictive modeling); exception
analysis, designed to identify and interpret anomalies in the found patterns.
Sometimes an intermediate stage of checking the reliability of the found patterns between their
discovery and use (validation stage) is explicitly identified. To analyze data on the spacecraft's O-
rings and compare the analysis results with the conclusions of the investigative commission, the
specialized Orange program was used [6].
3. Research methodology
The methods used by the authors to study the causes of failure of the sealing rings of the spacecraft
fuel tank were methods from a modern scientific direction - intelligent data analysis (IDA). In this
case, the IDA technology contains a number of stages: identifying patterns (free search); using the
identified patterns to predict unknown values (predictive modeling); analysis of exceptions
designed to identify and interpret anomalies in the found patterns. Based on the available set of
open experimental data on spacecraft flights by year and the corresponding number of failures of
the sealing rings of the fuel tank under different external conditions, the authors used regression
analysis - logistic regression, as well as the k-means data clustering method. These methods have
proven themselves to be successful in conducting statistical studies [7].
4. Development of a prediction model for O-ring failures in the
Orange program
The Orange analytical system is a program with open source code for machine data processing and
data visualization, which has a large set of advanced functions. The Orange software product,
which is developed by the Laboratory of Bioinformatics at the University of Ljubljana, is intended
for intelligent data analysis, statistical research and data visualization [8]. The method of modeling
is the creation of an expert system for predicting the probability of ring failure at given outside
temperatures, presented before analysis, based on criteria known from additional statistical data of
failures during previous flights. At the initial stage, the system that is fragmented uses logistic
regression algorithms [8,9]. We begin by adding a statistics file to the program (Fig. 3, a). The main
goal is to predict the probability of malfunctions, i.e. ring breakage. To achieve this, we will use the
variable Y, which we will define as the target (breakdown prediction). It should be noted that a
value of 0 indicates that the ring has no breaks, while a value of 1 indicates that it is broken. Next
comes the stage of connecting to File in our Data Table widget project for convenient display and
analysis of available data (Fig. 3). Based on the results of the data analysis of the table, it can be
seen that the first five rows (five rings) show the results of the first experiment (flight), identifying
the ring that was damaged at a temperature of 53 degrees (three rings). The next five rows
presented the results of other experiments conducted at 57 degrees (1 ring), and so on. Added a
new module Distributions for visualization, which will allow us to avoid that a larger number will
end up intact (Fig. 4). In Fig. 5 it is shown as a variable Y (value 0 110 rings are intact, 10 rings are
broken during the entire flight).
Figure 3: Adding the first file with statistical data and connecting a statistical data table to a file in
the Orange program
Figure 4: Adding a data visualization and analysis module Distributions
Figure 5: Y graph
It can be noted that previous flights were conducted at temperatures ranging from 50 to 90
degrees Fahrenheit, analyzed using the X variable (Figure 6). To investigate changes in the
probability of failure occurrence as a function of temperature, temperature can be classified based
on failure categories (Fig. 7). We will connect the Logistic Regression and Testing and Evaluation
modules together. The Logistic Regression module is connected to the primary file (Fig. 8). The
adequacy indicator of the model AUC = 0.74 (see Fig. 7). The AUC indicator (Area Under the ROC
Curve) is a measure that allows you to summarize the performance of the model in a single
number, measuring the area under the ROC curve. AUC ranges from 0 to 1, where a higher value
of AUC indicates a higher performance of the obtained mathematical model [10].
It is known that the logistic regression or logit model used in the program is a statistical model
used to predict the probability of an event occurring by comparing it with a logistic curve. This
regression produces an answer in terms of the probability of a binary event (1 or 0). Logistic
regression is used to predict the probability of an event occurring based on the values of a set of
attributes. To do this, a so-called dependent variable is introduced, which takes only one of two
values - as a rule, these are the numbers 0 (the event did not occur) and 1 (the event did occur), and
a set of independent variables (also called signs, predictors or regressors) - real, on based on the
values of which it is necessary to calculate the probability of accepting a particular value of the
dependent variable [11].
Figure 6: Graph by X (air temperature at shuttle launch)
Figure 7: Relation of faults to temperature (1 ring is working, 2 faulty)
Figure 8: Testing and evaluations
The next stage is the inclusion of a file with temperature data on the day of launch to the
program and the setting of the necessary parameters (Fig. 9). In this file, the parameters at a
temperature of 36 degrees are specified, which are used to predict the probability of ring breakage.
Figure 9: Files with new data
Display of predictions, or "Predictions", will allow us to predict the probability of failure by
analyzing the data from the file using the results obtained from the regression analysis and result
of predicting the failure of one ring (Chances of ring failure at 36 degrees Fahrenheit) is shown in
Fig.10.
\
Figure 10: General connection of widgets in the program and chances of ring failure at 36 degrees
Fahrenheit
The analysis showed that at a temperature of 36 degrees, the probability of failure of one ring
expert system does not recommend launching the shuttle at this temperature. The obtained result
fully confirms the conditions of the launch and the subsequent disaster. Thus, it is known that the
morning of the next day, January 28, turned out to be unusually cold - the temperature dropped to
a temperature of 12 °C. The low temperature caused concern among Thiokol engineers. During a
closed televised conference between Thiokol and NASA management, they expressed concerns that
such extreme conditions could adversely affect the elasticity of the solid rocket booster O-rings,
since the connections had not been tested at temperatures below 12 °C, and recommended delaying
the launch [12, 13]. At the same time, the problem of the O-rings had not yet been solved and was
of the highest criticality, engineers doubted that both rings would be able to maintain the tightness
of the connection at low temperatures. Thiokol engineer Roger Beaujoli insisted on the immediate
cancellation of the launch. Thiokol management supported its engineers, but due to constant
delays, NASA management was categorically against it. They dismissed concerns about the
inelasticity of the ring, groundlessly arguing that if the main ring could not provide a seal, the
backup ring would do so [14, 15, 16]. Seeing NASA's categorical attitude, the Thiokol management
gave in and gave permission for the launch, which led to the subsequent destruction of the rings,
fuel leakage and fire of the ship's fuel tank [17, 18, 19].
For additional analysis of the causes of the disaster, the authors used an additional data mining
method k-means. The k-means method is the most popular clustering method. It was invented in
the 1950s by mathematician Hugo Steinhaus [20] and almost simultaneously by Stuart Lloyd [21].
It gained particular popularity after the work of MacQueen [22].
The action of the algorithm is such that it seeks to minimize the total squared deviation of the
cluster points from the centers of these clusters:
𝑘
𝑉 = ∑ ∑ ) (𝑥 − 𝜇𝑖 )2
𝑖=1 𝑥∈𝑆𝑖
where 𝑘 is the number of clusters, 𝑆i are the obtained clusters, 𝑖 𝑘, and 𝜇𝑖 are the
centers of mass of all vectors 𝑥 from cluster 𝑆𝑖
The k-means method is used for data clustering based on an algorithm for partitioning a
vector space into a predetermined number of clusters k. The algorithm is an iterative procedure
in which the following steps are performed:
1. The number of clusters k is selected.
2. From the initial data set, k observations are randomly selected to serve as the initial
cluster centers.
3. For each observation of the initial set, the closest cluster center is determined (distances
rtain center
form the initial clusters.
4. Centroids are calculated the centers of gravity of the clusters. Each centroid is a vector
whose elements are the average values of the corresponding features calculated for all records
of the cluster.
5. The cluster center is shifted to its centroid, after which the centroid becomes the center of
a new cluster.
6. The 3rd and 4th steps are repeated iteratively. Obviously, at each iteration, the boundaries
of the clusters change and their centers shift. As a result, the distance between elements within
the clusters is minimized and the intercluster distances increase.
The algorithm stops when the cluster boundaries and centroid locations do not stop
changing from iteration to iteration, i.e., at each iteration, each cluster will contain the same set
of observations. In practice, the algorithm usually finds a set of stable clusters in several dozen
iterations.
To implement the presented algorithm, the K-means widget is used (Fig. 11).
Figure 11: Software implementation of the k-means method
The window for demonstrating the result of the method is shown in Fig. 12.
Figure 12: The result of the data classifier analysis method
Visual analysis of the data shows the highest number of ring failures (marked 1 in Fig. 12) at
low temperatures, i.e. below 54 degrees Fahrenheit (12 degrees Celsius). Thus, it can be concluded
that low air temperatures significantly affected the elasticity of the fuel tank rings.
5. Conclusions
The performed analysis of the use of regression modeling to predict the probability of failure of the
space shuttle Challenger sealing rings emphasizes the high efficiency of this method for predicting
potential emergency situations. During the research, tools such as Data Table, Distributions,
Logistic Regression, and Test and Score were used, which allowed to systematically analyze the
provided data.
The degree of adequacy of the mathematical model implementing the logistic regression method
was 74%, which indicates an acceptable result of the data obtained.
The obtained results indicate that at a temperature of 36 degrees Celsius there is an 89%
probability of ring failure, which indicates a significant risk of defects. Overall, the average
probability of failure for
such a configuration in flight conditions. The limitation of the proposed approach, according to
the authors, is the need for a large array of statistical experimental data on defects in the fuel
elements of spacecraft.
The application of regression analysis in aerospace programs can serve as an important tool for
reducing risks, increasing the level of safety of space missions, and contributing to the
development of national and international space initiatives [23]. The proposed model allows
predicting with a high degree of accuracy the possible failure of fuel tank elements of modern
spacecraft. As a prospect for further research, it is possible to analyze the causes of accidents of
experimental spacecraft for flights to Mars of the Elon Max company in the presence of accessible
statistical data using the universal mathematical models proposed by the authors implementing the
IAD methods.
References
[1] Why hasn't SpaceX landed humans on the Moon yet?. URL: https://www.quora.com/Why-
hasnt-SpaceX-landed-humans-on-the-Moon-
yet#:~:text=It%20builds%20rockets%20for%20money,could%20land%20on%20the%20Moon.
[2] Shuttle_Challenger_disaster, 2024. URL:
//https://en.wikipedia.org/wiki/Space_Shuttle_Challenger_disaster.
[3] V.S. Mikhailenko, R.Yu. Kharchenko, Analysis of traditional and neuro-fuzzy adaptive
systemof controlling the primary steam temperature in the direct flow steam generators in
TPS. Automatic Control and Computer Sciences 48 6 (2014) 334 344. doi:
10.3103/S0146411614060066.
[4] V.S. Mikhaylenko, R.Y. Kharchenko, V.A. Shcherbinin, Analysis of the Predicting Neural
Network Person Recognition System by Picture Image. Automatic Control and Computer
Sciences, 54 3 (2020) 249 258. doi:10.3103/s0146411620030037.
[5] V.S. Mikhailenko, R.Yu. Kharchenko, V.A. Shcherbinin, V.V. Leshchenko, Using Neural
Network Technologies to Simulate the Working Processes of Ship Steam Boilers CEUR
Workshop Proceedingsthis link is disabled, urn:nbn:de:0074-3126-7, 2021, 3126, pp. 367–373.
[6] Data Mining Fruitful and Fun. URL: https://orangedatamining.com/
[7] K-means. URL: https://en.wikipedia.org/wiki/K-means_clustering
[8] R.Yu. Kharchenko, A.V. Kochetkov, V.S. Mikhaylenko, Analysis of methods for automated
research of dc voltage converters of modular structure. Radio Electronics, Computer Science,
Control 3 7 (2022). doi:10.15588/1607-3274-2022-3-1.
[9] R.Yu. Kharchenko, V.S. Mykhailenko, A.V. Kochetkov, Development of a Neuro-Fuzzy
Intelligent Network for Monitoring and Control of Microclimate Systems. Automatic Control
and Computer Sciences, 57 1 (2023) 27 36. doi:10.3103/s0146411623010066.
[10] SAP ANALYTICS CLOUD, 2024. URL: //https://help.sap.com/docs/SAP_ANALYTICS_CLOUD
[11] V. Mezhuyev, Y. Gunchenko, V. Mikhailenko, R. Kharchenko, V. Leshchenko, Mathematical
Simulation Process Increasing Energy Efficiency of Ship Steam Boilers., in: Proceedings of the
11-th International Conference "Information Control Systems & Technologies" Odesa, Ukraine,
September 21–23, 2023, pp. 141-152.
[12] A. Z. Spector, Achieving application requirements, in: S. Mullender (Ed.), Distributed Systems,
2nd. ed., ACM Press, New York, NY, 1990, pp. 19 33. doi:10.1145/90417. 90738.
[13] The Space Shuttle Challenger Disaster. Risk Revealed, (2024) 13 23.
doi:10.1017/9781009299794.004
[14] J.M. Logsdon, Truths, Lies, and O-Rings: Inside the Space Shuttle Challenger Disaster. Space
Policy, 25 4 (2009) 269 269. doi:10.1016/j.spacepol.2009.09.002
[15] Challenger missions 1983 1986. (n.d.). Springer Praxis Books, (2007) 273 282. doi:10.1007/978-
0-387-49679-5_7
[16] The Space Shuttle Challenger Disaster. Saving Human Lives, (2005) 107 153. doi:10.1007/1-
4020-2980-2_7
[17] Space Shuttle Challenger. Springer Praxis Books. Springer New York, (2007). doi:10.1007/978-
0-387-49679-5
[18] J. Garten, The Space Shuttle Challenger Accident. (2011). doi:10.4135/9781526406958
[19] S. Pomeroy, Truth, Lies, and O-Rings: Inside the Space Shuttle Challenger Disaster.
Technology and Culture, 51 4 (2010) 1038 1040. doi:1353/tech.2010.0077
[20] Recovery after Challenger. (n.d.). Springer Praxis Books in Space Exploration, 99 146.
doi:10.1007/978-0-387-73972-4_3
[21] H. Steinhaus On the division of material bodies into parts. Bull. Acad. Polon. Sci., C1. III IV
(1956): 801 804.
[22] S. Lloyd, Least square qua (1957).
[23] J. MacQueen Some methods for classification and analysis of multivariate observations. in:
Proc. 5th Berkeley Symp. on Math. Statistics and Probability, 1967, pp.281-297.
[24] V. Vychuzhanin, N. Rudnichenko, Z. Sagova, M. Smieszek, V. V. Cherniavskyi, A. I. Golovan,
M. V. Volodarets, Analysis and structuring diagnostic large volume data of technical condition
of complex equipment in transport. IOP Conference Series: Materials Science and Engineering,
Volume 776, 24th Slovak-Polish International Scientific Conference on Machine Modelling and
Simulations - MMS 2019, 3-6 September 2019, Liptovský Ján, Slovakia, 2019 pp.1-11.
DOI:10.1088/1757-899X/776/1/012049.