=Paper= {{Paper |id=Vol-3790/paper15 |storemode=property |title=A method of cost-effective operation of equipment in the aviation enterprise with using intelligent technologies |pdfUrl=https://ceur-ws.org/Vol-3790/paper15.pdf |volume=Vol-3790 |authors=Zarina Poberezhna,Maksym Zaliskyi |dblpUrl=https://dblp.org/rec/conf/icst2/PoberezhnaZ24 }} ==A method of cost-effective operation of equipment in the aviation enterprise with using intelligent technologies== https://ceur-ws.org/Vol-3790/paper15.pdf

A method of cost-effective operation of equipment in the
aviation enterprise with using intelligent technologies
Zarina Poberezhna1 and Maksym Zaliskyi1
1
National Aviation University, Liubomyra Huzara Ave. 1, Kyiv, 03058, Ukraine

Abstract
Civil aviation enterprises usually spend significant financial resources to support the operation of aviation
equipment. Most of these funds are related to the salary of the personnel of the operating companies, the
purchase of auxiliary equipment for the operational processes, storage and transportation of spare parts of
the equipment. These costs form the tariff rates for maintenance and repair procedures by the types of used
equipment. In the general case, total operational costs and specific costs are determined by these tariff rates.
Too frequent maintenance, on the one hand, leads to a high level of reliability, but, on the other hand, is
characterized by significant operational costs. A small number of maintenance activities reduces the
reliability of the equipment and is also characterized by high total costs due to a much higher repair tariff
rate. In this context, the use of intelligent technologies in the management system of maintenance and
repair processes for aviation equipment allows to improve the procedures for forming and making timely
decisions. This paper considers the problem of determining the optimal time moment for maintenance
carrying out in terms of minimizing operational costs. The problem is solved analytically, taking into
account the stochastic model of changes in the diagnostic variable in the period immediately before the
equipment failure. The obtained analytical relations are confirmed by statistical modeling.

Keywords
Intelligence technologies, aviation enterprise, operational cost optimization, equipment operation,
maintenance, repair, data processing1

1. Introduction
The protection of human life and material assets is the main task of civil aviation [1]. To achieve this
task, a set of organizational, technical and regulatory factors has been developed to minimize the
risks of aviation accidents associated with the acts of unlawful interference, serviceability and
reliability of technical equipment, human factors, and others [2, 3].
Today, civil aviation is a system of interconnected systems aimed at ensuring the flight operations
of aircraft. It has a hierarchical structure, which includes aircraft, equipment, resources, aviation
enterprises, and others.
Modern intelligence technologies play a key role in optimizing processes at aviation enterprises.
Due to the rapid development of the aviation industry, increasing efficiency and reducing costs are
becoming a priority for aviation enterprises. The use of intelligent control systems allows automating
routine processes, which significantly reduces the likelihood of human error and increases the
accuracy of operations. These technologies also contribute to improved forecasting and planning,
providing more accurate and timely decision-making.
It is worth noting that analytical tools based on artificial intelligence (AI) are capable of
processing large arrays of data, identifying hidden patterns and trends, which contributes to the
strategic development of enterprises. In addition, intelligent technologies can improve safety by
monitoring and analyzing the technical condition of aircraft in real time. This allows for early
detection of potential malfunctions and prevention of emergencies. The implementation of such

ICST-2024: Information Control Systems & Technologies, September, 23 25, 2024, Odesa, Ukraine
Corresponding author.
These authors contributed equally.
zarina_www@ukr.net (Z. Poberezhna); maximus2812@ukr.net (M. Zaliskyi)
0000-0001-6245-038X (Z. Poberezhna); 0000-0002-1535-4384 (M. Zaliskyi)
© 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
solutions helps to increase the competitiveness of enterprises in the global market. In general,
modern intelligent technologies are an integral part of the successful operation and development of
aviation enterprises in the modern world.
The use of modern intelligent technologies in the system of maintenance and operation of
aviation equipment is a promising area that, unfortunately, has not yet been sufficiently explored.
Despite the obvious benefits, such as improved diagnostic accuracy, fault prediction, and
optimized maintenance, the lack of empirical data makes it difficult to implement intelligent systems
in practice. This leads to the fact that aviation enterprises are forced to rely on traditional
maintenance methods that are less efficient and more resource-intensive. In addition, insufficient
attention to the integration of the new technologies can reduce the level of flight safety and reliability
of aviation equipment. Further research is needed that would cover various aspects of the use of
intelligent technologies, from data collection and analysis to real-life examples of their application
in the aviation industry. This would allow for the development of clear recommendations and
standards for aviation enterprises, especially in reducing the cost of operating equipment.
Thus, expanding research in this area is critical to improving the efficiency and safety of
maintenance and operation of aviation equipment.

2. State of the art and the statement of the problem
Integration of modern management technologies allows automated monitoring of equipment
condition, increasing diagnostic accuracy and timeliness of repairs. The use of analytical tools helps
to predict and prevent possible malfunctions, which significantly reduces risks. In addition,
optimized business processes contribute to the efficient use of resources and reduce maintenance
costs. The implementation of the standards and regulations in maintenance processes ensures
compliance with international safety requirements [4, 5].
The article [6] determines that intelligent technologies for managing the processes of
maintenance and operation of airline equipment, which use artificial intelligence, automation, big
data analysis and other advanced technologies for optimization, receive greater financial benefits
than those that use traditional methods of equipment operation.
Improving the financial position of the airline company can be achieved through better quality of
services, resource-saving methods, material and technical development, and social programs [7].
Paper [8] established that aviation enterprises that enter the market with innovative business
processes and an appropriate competitive strategy have significant market advantages and can set
their own rules with a strong competitive advantage over competitors.
An analysis of the literature in the field of aviation equipment operation shows that researchers
are currently focused on the following problems:

1. Reliability analysis at the stages of operation beyond the determined useful life of the
equipment [9, 10].
2. Development of intelligent data processing methods using artificial intelligence tools [11, 12].
3. Minimization of expendable resources [13, 14].
4. Synthesis and analysis of decision-making procedures regarding the condition of equipment
and components of the operating enterprise [15, 16].
5. Optimizing the spare parts inventory and the organizational structure of their deployment
[17, 18].
6. Optimization of operation processes [19, 20].

Civil aviation enterprises usually spend significant financial resources to support the operation
of aviation equipment [21]. Most of these costs are related to the remuneration of the personnel of
the operating companies, the purchase of auxiliary equipment for the operation processes, storage
and transportation of spare parts of equipment. These costs form the tariff rates for maintenance 𝐶𝑀
and repair 𝐶𝑅 procedures by the type of used equipment [22, 23].
When solving the problem of minimizing operational costs, it is quite logical to choose the total
operating costs 𝐶Σ or average specific operating costs 𝐸(𝐶Σ ⁄𝑇Σ ) per observation interval 𝑇Σ as an
indicator of efficiency [24, 25].
Average specific operational costs can be minimized based on the following considerations. Too
frequent maintenance, on the one hand, leads to a high level of reliability, but, on the other hand, is
characterized by high operational costs. A small number of maintenance activities reduces the
reliability of the equipment and is also characterized by high total costs due to a much higher repair
tariff rate. Therefore, the optimal number of maintenance procedures can be determined. This
approach fits within the framework of the concept of preventive maintenance [26].
During the preventive maintenance, the decision to perform it is made on the basis of data
processing using datasets on diagnostic variables of the equipment. We assume that these parameters
are characterized by a stochastic model 𝑀𝐷𝑉 and the processing algorithms form a vector 𝐴⃗. We
assume that the cost of implementing one processing procedure is spent with a tariff rate 𝐶𝑃 .
Equipment failure occurs when the values of the diagnostic variables exceed the operational
tolerance 𝑣𝑂 and the preventive maintenance is implemented when variables exceed the preventive
tolerance 𝑣𝑀 . The process of failure is characterized by a stochastic model 𝑀𝐹 . In addition, a number
of limitations may be imposed on the aviation company-operator, which are characterized by the set
𝐿. Therefore, the efficiency indicator can be represented as the functional dependence of the
following form
𝐸(𝐶Σ ⁄𝑇Σ ) = Ψ(𝑀𝐷𝑉 , 𝑀𝐹 , 𝐴⃗, 𝐶𝑀 , 𝐶𝑅 , 𝐶𝑃 , 𝑣𝑂 , 𝑣𝑀 |𝐿 ). (1)
The main task is to determine a preventive tolerance that minimizes the average specific
operating costs, i.e.
𝐸(𝐶Σ ⁄𝑇Σ )𝑜𝑝𝑡𝑖𝑚𝑎𝑙 = min Ψ(𝑀𝐷𝑉 , 𝑀𝐹 , 𝐴⃗, 𝐶𝑀 , 𝐶𝑅 , 𝐶𝑃 , 𝑣𝑂 , 𝑣𝑀 |𝐿 ). (2)
𝑣𝑀
Thus, the purpose of this paper is to optimize the operational costs of the aviation enterprise
when monitoring the diagnostic variables of equipment. In this case, the main attention will be paid
to the analytical solution of the optimization problem within the framework of the assumptions made
about the models of the diagnostic variables, the model of failure occurrence and the adopted data
processing algorithms.

3. Intelligence technologies during equipment operation
The use of intelligent technologies in the management system of maintenance and operation of
equipment at aviation enterprises helps to increase productivity, reduce costs, and improve the
overall competitiveness of the enterprise in the global market. The key opportunities for their
application in the aviation sector are summarized in Table 1. In the context of the research problem,
it is possible to identify the key advantages of using modern intelligent technologies in the
management system of maintenance and operation of aviation equipment:

1. Because of the use of intelligent technologies (AI and big data analysis), it is possible to
accurately predict possible failures and plan their elimination in time, which reduces
equipment downtime and increases its availability.
2. Intelligent systems allow for efficient management of spare parts, rational allocation of
personnel time, and minimization of maintenance costs.
3. With real-time monitoring and response systems, potential security threats can be quickly
identified and timely remedial action can be taken.
4. Automation of routine tasks and the use of intelligent systems help free up time
to perform more complex and strategic tasks, which increases their efficiency and job
satisfaction.
5. The integration of various data and analytical tools allows to optimize the maintenance and
operation schedule, coordinate the work of various departments, and minimize equipment
downtime through optimal planning.
6. Failure prediction systems and regular monitoring of equipment conditions allow to timely
detect and eliminate problems, which increases equipment availability.
7. The use of intelligent systems allows to efficient collect, process, and analyze operational
data, which helps to improve reporting and make informed decisions.
8. Real-time monitoring and analysis of data allows to quickly identify problems and changes
in the condition of the equipment, which can help to quickly respond and take the necessary
measures to solve them [27 29].

Table 1
Possibilities of using modern intelligent technologies in the management system for maintenance
and operation of aviation equipment
Areas of Characteristics of intelligent technologies
application
1. Fault prediction 1.1. AI. The AI algorithms can analyze historical data on equipment
maintenance and operation to predict possible malfunctions. This
allows to plan repairs in advance, reducing the aviation risks.
1.2. Big Data and analytics. Analyzing large arrays of data allows to
identify patterns and trends that may indicate impending failures.
2. Real-time 2.1. Internet of Things. The sensors installed on equipment provide
monitoring continuous data collection on its condition. This allows real-time
monitoring of systems and prompt response to any deviations.
2.2. Cloud technologies. Data can be transferred to cloud storage,
where it is processed and analyzed for immediate response.
3. Automation of 3.1. Process robotization. These technologies can automate routine
business processes tasks, such as document management, processing spare parts orders,
scheduling maintenance, and other operation processes.
3.2. Integrated management systems. The use of enterprise resource
planning system can automate and coordinate all aspects of
maintenance.
4. Improved 4.1. Virtual and augmented reality. It can help to train personnel, to
training and simulate real-life situations and practice maintenance skills without
support risking real equipment.
4.2. Intelligent learning platforms. AI-powered learning systems can
adapt training programs to the individual needs of each employee.
5. Optimization of 5.1. Inventory optimization. AI and analytical tools help to optimize
resources spare parts inventory management, reducing storage costs and
preventing shortages of critical components.
5.2. Resource planning and allocation. Intelligent systems can
automatically schedule and allocate work, ensuring
optimal use of human resources.
6. Ensuring 6.1. Blockchain. The use of blockchain technologies to track the service
compliance with history and origin of spare parts ensures transparency and compliance
standards with international standards and regulations.
6.2. Automated reporting systems. It helps to ensure compliance with
aviation regulatory requirements.
7. Improving 7.1. Intelligent platforms for customers. AI-powered systems can
customer provide customers with information on the status of their equipment
experience maintenance, predicted completion dates, and other important
information in real time.
7.2. Personalized services. Using AI to analyze customer needs allows
to provide personalized services and recommendations.
Thus, the introduction of modern intelligent technologies in the aviation equipment maintenance
and operation management system is a critical step for airlines to improve the efficiency, safety and
reliability of their operations, reduce costs and increase customer satisfaction with flight services.

4. Method of operational costs optimization
Let us consider the problem of optimizing operational costs for the case of monitoring the diagnostic
variables of aviation equipment. To do it, we will accept a number of restrictions on the parameter
models and the failure process:

1. One diagnostic variable is subject to monitoring. Let this variable be the voltage at a certain
control point of aviation radio equipment.
2. Monitoring is performed discretely with a sampling interval of ∆ and the cost of a single
processing procedure 𝐶𝑃 .
3. The diagnostic variable has two components: deterministic and random. The deterministic
component has the form of a steady signal that corresponds to the standard initial value 𝑢0 .
The random component is caused by the influence of thermal noise, instability of power
supplies, and the presence of errors in measuring equipment.
4. The random component 𝑛𝑖 of the diagnostic variable can be characterized by a normal
distribution law with zero mathematical expectation and a given standard deviation 𝜎.
5. The pre-failure state is characterized by a violation of the stationarity of the trend of the
diagnostic variable change. The transition from the state of normal functioning to this state
occurs at a random moment of time 𝑡𝐵𝐹 . The value of 𝑡𝐵𝐹 can be described by a uniform
distribution law in the range of possible values [𝑡𝐵𝐹 𝑚𝑖𝑛 ; 𝑡𝐵𝐹 𝑚𝑎𝑥 ].
6. The pre-failure state causes a linear change in the diagnostic variable over the time. The
slope of the linear trend 𝛾 is random. The value of 𝛾 can be described by a uniform
distribution law in the range of possible values [𝛾𝑚𝑖𝑛 ; 𝛾𝑚𝑎𝑥 ].
7. Failures are independent, which does not require additional procedures for processing
correlation dependencies [30, 31].
8. The duration of preventive maintenance is a deterministic value and is equal to 𝜏.
9. After maintenance or repair, the value of the diagnostic variable will be equal to the value
𝑢0 .

Based on these assumptions, a mathematical model of the diagnostic variable can be written in a
discrete form
𝑢𝑖 = 𝑢0 + 𝑛𝑖 + tan (𝛾)(𝑖 − 𝑡𝐵𝐹 )𝜙(𝑖 − 𝑡𝐵𝐹 ), (3)
where 𝜙(𝑖) is the step function.
To evaluate this model, regression analysis methods can be used, in particular those studied in
[32 34].
For the cases when 𝑖 < 𝑡𝐵𝐹 , the diagnostic variable will be described by a normal law with mean
𝑢0 and standard deviation 𝜎 because of linear functional transformations. In the case of the pre-
failure condition, taking into account the independence of the set of random variables 𝑛𝑖 , 𝑡𝐵𝐹 and 𝛾
and using the methods of functional transformations, the probability density function of the
diagnostic variable can be presented as follows
𝑓(𝑛)
𝑓(𝑢) = ∫ ∫ 𝑓(𝑡𝐵𝐹 )|𝑡 =𝑖−𝑢−𝑢0 −𝑛 𝑑𝑛𝑑𝛾. (4)
tan (𝛾) 𝐵𝐹 tan(𝛾)
The efficiency indicator (1) can be written in the following form
𝑞𝑀 𝐶𝑀 + 𝑞𝑅 𝐶𝑅
𝐸(𝐶Σ ⁄𝑇Σ ) = 𝑞𝑀 , (5)
∑𝑗=1 𝑡𝑀 𝑗 + 𝑞𝑀 𝜏 + ∑𝑞𝑗=1
𝑀
𝑡𝐹 𝑗 + 𝑞𝑅 𝑡𝑅
where 𝑞𝑀 and 𝑞𝑅 are the average number of maintenance and repair procedures per observation
interval, 𝑡𝑀 𝑗 and 𝑡𝐹 𝑗 are the moments when the diagnostic variable exceeds the preventive and
operational thresholds, 𝑡𝑅 is the average repair duration.
In this view, it can be assumed that failure will occur when there is insufficient time to perform
preventive maintenance in the case if the diagnostic variable changes from the preventive to the
operational threshold. To solve the problem (2), we use model (3) for the moments of time 𝑡𝑀 𝑗 and
𝑡𝐹 𝑗 , resulting in

𝑣𝑀 − 𝑢0 𝑛̃𝑖
𝑡𝑀 𝑗 = + 𝑡𝐵𝐹 − , (6)
tan (𝛾) tan(𝛾)
𝑣𝑂 − 𝑢0 𝑛𝑖
𝑡𝐹 𝑗 = + 𝑡𝐵𝐹 − , (7)
tan (𝛾) tan(𝛾)
where 𝑛̃𝑖 is the error value of the measuring equipment at the moment of exceeding the preventive
threshold.
The maximum time resource for failure elimination and preventive maintenance carrying out will
be determined as
𝑣𝑂 − 𝑣𝑀 + 𝑛̃𝑖 − 𝑛𝑖
𝑡𝑚𝑎𝑥 𝑗 = 𝑡𝐹 𝑗 − 𝑡𝑀 𝑗 = . (8)
tan(𝛾)
Let's assume that the monitoring uses equipment of a high accuracy class, then σ ≪ 1. Then
formula (8) will be simplified
𝑣𝑂 − 𝑣𝑀
𝑡𝑚𝑎𝑥 𝑗 ≈ . (9)
tan(𝛾)
To find the estimates 𝑞𝑀 and 𝑞𝑅 we first determine the probability density function for 𝑡𝑚𝑎𝑥 𝑗 .
To do this, we write the inverse function to (9)
𝑣𝑂 − 𝑣𝑀
𝛾 = arctan ( ). (10)
𝑡𝑚𝑎𝑥 𝑗
In this case, the Jacobian of the transformation is equal to the modulus of the derivative of
function (10) and is equal to
𝑑𝛾 𝑣𝑂 − 𝑣𝑀
𝐽=| |= 2 . (11)
𝑑𝑡𝑚𝑎𝑥 𝑗 𝑡𝑚𝑎𝑥 𝑗 + (𝑣𝑂 − 𝑣𝑀 )2
Therefore
1 𝑣𝑂 − 𝑣𝑀
𝑓(𝑡𝑚𝑎𝑥 𝑗 ) = 2 . (12)
𝛾𝑚𝑎𝑥 − 𝛾𝑚𝑖𝑛 𝑡𝑚𝑎𝑥 𝑗 + (𝑣𝑂 − 𝑣𝑀 )2
The average number of maintenance and repair procedures is determined from density (12) by
determining the integral from zero to 𝜏 and the integral from 𝜏 to infinity. As a result of solving the
equations, we can get the estimates of the form
𝑞Σ 𝜋 𝑡𝑚𝑎𝑥 𝑗
𝑞𝑀 = ( − 𝛾𝑚𝑖𝑛 − arctan ( )), (13)
𝛾𝑚𝑎𝑥 − 𝛾𝑚𝑖𝑛 2 𝑣𝑂 − 𝑣𝑀
𝑞Σ 𝑡𝑚𝑎𝑥 𝑗 𝜋
𝑞𝑅 = (arctan ( ) − + 𝛾𝑚𝑎𝑥 ), (14)
𝛾𝑚𝑎𝑥 − 𝛾𝑚𝑖𝑛 𝑣𝑂 − 𝑣𝑀 2
where 𝑞Σ is the average total number of maintenance and repair procedures.
After using the operations of averaging, finding the statistical characteristics of the tangent of the
random slope of the linear trend and using equations (13) and (14), the observation interval
(numerator in equation (5)) can be presented as follows
𝑡𝑚𝑎𝑥 𝑗 𝑡𝑚𝑎𝑥 𝑗
𝑇Σ = 𝑞Σ (𝑎1 + 𝑎2 𝑣𝑀 + 𝑎3 arctan ( ) + 𝑎4 𝑣𝑀 arctan ( )), (15)
𝑣𝑂 − 𝑣𝑀 𝑣𝑂 − 𝑣𝑀
where 𝑎1 , 𝑎2 , 𝑎3 , 𝑎4 are the values determined using the initial parameters
𝑡𝐵𝐹 𝑚𝑖𝑛 + 𝑡𝐵𝐹 𝑚𝑎𝑥
𝑎1 = −
2
sin 𝛾
sin 𝛾𝑚𝑎𝑥 𝜋 𝜋 𝑣𝑂 ln ( sin 𝛾𝑚𝑎𝑥 )
𝑚𝑖𝑛
𝑢0 ln ( sin 𝛾 ) + 2 𝜏 − 𝛾𝑚𝑖𝑛 𝜏 + (2 + 𝛾𝑚𝑎𝑥 ) (𝑡𝑅 − + 𝛾 )
𝑚𝑖𝑛 𝑚𝑎𝑥 − 𝛾𝑚𝑖𝑛
− ,
𝛾𝑚𝑎𝑥 − 𝛾𝑚𝑖𝑛
𝜋 (16)
𝑎2 = 2 − 𝛾𝑚𝑖𝑛 ln (
sin 𝛾𝑚𝑎𝑥
),
(𝛾𝑚𝑎𝑥 − 𝛾𝑚𝑖𝑛 ) 2 sin 𝛾𝑚𝑖𝑛
sin 𝛾𝑚𝑎𝑥
𝑣𝑂 ln ( ) + 𝑡𝑅 − 𝜏
sin 𝛾𝑚𝑖𝑛
𝑎3 = ,
(𝛾𝑚𝑎𝑥 − 𝛾𝑚𝑖𝑛 )2
1 sin 𝛾𝑚𝑎𝑥
𝑎4 = 2
ln ( ).
{ (𝛾𝑚𝑎𝑥 − 𝛾𝑚𝑖𝑛 ) sin 𝛾𝑚𝑖𝑛

If we introduce additional values 𝑎5 and 𝑎6 of the type
𝜋 𝜋
𝐶𝑅 (𝛾𝑚𝑎𝑥 − 2 ) − 𝐶𝑀 (2 − 𝛾𝑚𝑖𝑛 )
𝑎5 = ,
(𝛾𝑚𝑎𝑥 − 𝛾𝑚𝑖𝑛 )2 (17)
𝐶𝑅 − 𝐶𝑀
𝑎6 = ,
{ 𝛾𝑚𝑎𝑥 − 𝛾𝑚𝑖𝑛
then we obtain the final equation for the efficiency indicator (5) in the form of average total specific
costs
𝑡𝑚𝑎𝑥 𝑗
𝑎5 + 𝑎6 arctan (𝑣 − 𝑣 )
𝐸(𝐶Σ ⁄𝑇Σ ) = 𝑂 𝑀 (18)
𝑡𝑚𝑎𝑥 𝑗 𝑡𝑚𝑎𝑥 𝑗 .
𝑎1 + 𝑎2 𝑣𝑀 + 𝑎3 arctan (𝑣 − 𝑣 ) + 𝑎4 𝑣𝑀 arctan (𝑣 − 𝑣 )
𝑂 𝑀 𝑂 𝑀
The last step of the calculation is to analyze function (18) for the presence of a minimum with
respect to the preventive threshold 𝑣𝑀 . The analysis showed that this dependence has one minimum.
The complex nature of formula (18) necessitated the use of numerical optimization methods to find
the optimal preventive threshold.
The flowchart of proposed method is shown in Figure 1.

Analysis of restrictions Calculation of the moments
Building the
on the parameter when the diagnostic variable
mathematical model of
models and the failure exceeds the preventive and
the diagnostic variable
process operational thresholds

Determining the Statistical analysis of Calculation of the maximum
average number of maximum time time resource for failure
maintenance and repair resource for failure elimination and preventive
procedures elimination maintenance carrying out

Analysis of dependence of the
Procedures to obtain
efficiency indicator on the
the final equation for
preventive threshold for the
the efficiency indicator
presence of a minimum value

Figure 1: The flowchart of proposed method.
5. Results and discussions
For the practical testing of the proposed method of optimizing the operational costs of the aviation
enterprise, statistical modeling was carried out. The primary data for modeling were obtained by
analyzing statistical reports and activities of three aviation enterprises:

– ZEFKO UKRAINE LLC.
–
– PJSC "DHL International Ukraine" [35 37].

The central repair center for spare parts and equipment is located at a long distance from the
enterprises, which indicates a rather expensive and lengthy delivery of units with operational
equipment failures. Maintenance procedures were carried out at the airport, which makes it much
less expensive compared to repairs.
At the initial stage of the simulation, realizations of one diagnostic variable in the form of a 220
V supply voltage were obtained for 10000 repetition epochs. An example of changing the diagnostic
variable is shown in Figure 2.
The graph in Figure 2 was obtained for the initial data:

– Nominal value of the supply voltage 𝑢0 = 220 V.
– Operational thresholds 𝑣𝑂 = {176; 264} V.
– Preventive thresholds 𝑣𝑂 = {198; 242} V.
– Standard deviation of noise 𝜎 = 3 V.
𝜋 𝜋
– Distribution parameters for the slope angle: 𝛾𝑚𝑖𝑛 = 9 , 𝛾𝑚𝑎𝑥 = 3 .
– Distribution parameters for the moment of transition to the state before failure: 𝑡𝐵𝐹 𝑚𝑖𝑛 =
30, 𝑡𝐵𝐹 𝑚𝑎𝑥 = 80.
– The sampling interval is equal to one unit of time.

ui
Voltage

Discrete time
Figure 2: The example of diagnostic variable change before failure.

In Figure 1, we have one failure elimination when the voltage value does not exceed the
operational threshold in the deterioration state, and two failures when this threshold is reached.
The durations of threshold crossings are also random variables. Figure 3 shows a histogram of
frequencies for the moments of crossing the operational threshold.
The frequencies in interval getting

Discrete time

Figure 3: The histogram of time moments of operation threshold intersection.

The nature of the dependence of the statistical distribution in Figure 3 corresponds to the failure
model and can be characterized by asymmetric laws, such as Rayleigh, Weibull, Birnbaum-Saunders,
inverse Gaussian, and others. To determine the optimal preventive threshold, the Monte Carlo
method was used for such initial data:

– The cost of a single processing procedure 𝐶𝑃 = 0.01 USD.
– Cost of maintenance 𝐶𝑀 = 250 USD.
– The cost of repair 𝐶𝑅 = 10000 USD.
– Duration of maintenance 𝑡𝑀 = 24 hours.
– Duration of the repair 𝑡𝑅 = 72 hours.

The simulation results are shown in Figure 4.
The average operational cost per observation time

E(CΣ/TΣ)

Simulation result

Equation (18)

Preventive threshold

Figure 4: The average operational cost per observation time.
Figure 4 shows that the simulation results coincide with the analytical calculations. The minimum
points on both graphs also coincide. The optimal value of the preventive threshold, which
corresponds to the minimum operational cost, for the determined initial data is 257.48 V. The results
of calculations and statistical simulation demonstrate the feasibility of the proposed approach for
optimizing operational costs not only for aviation enterprises, but also in other industries.

6. Conclusions
Thus, the use of modern intelligent technologies in the management system for the maintenance and
operation of aviation equipment is very appropriate and promising in the practice of an aviation
enterprise. The introduction of intelligent technologies will allow aviation companies to optimize
costs, reduce equipment downtime and increase its availability. In addition, they provide improved
data management, which contributes to more efficient decision-making and resource planning.
Intelligent technologies help to ensure sustainable improvement and innovation in the management
system, which allows airlines to provide high quality service and remain competitive in the modern
world of the aviation industry. This paper considers the problem of determining the optimal
frequency of preventive maintenance in terms of minimizing operational costs. The problem is
solved analytically, taking into account the stochastic model of changes in the diagnostic variable in
the period immediately before the equipment failure. The obtained analytical equations are
confirmed by statistical simulation. The results of the study can be used during the launching new
aviation enterprises specialized on maintenance and operation and improve the management of
production processes for existing ones.
Future scientific research will be aimed at developing a maintenance system with an adaptive
preventive threshold, the value of which will be adjusted to the current trends in the diagnostic
variables of aviation equipment.

Acknowledgements
This research is partially supported by the Ministry of Education and Science of Ukraine under the
projects Methods of building protected multilayer cellular networks 5G / 6G based on the use of
artificial intelligence algorithms for monitoring critical infrastructure objects #
0124U000197) and is partially supported by EURIZON project # 871072 (Project EU #3035 EURIZON
Research and development of Ukrainian ground network of navigational aids for increasing the
safety of civil aviation .

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