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Determination of a model of preventive maintenance of special purpose vehicles
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^ Stojko Lj. Biocanin3, Milica S. Timotijevicb,
> Zeljko M. Bulatovicc, Milan A. Misicd
jf a Academy of Applied Technical Studies Belgrade, Department of o
° Computer and Mechanical Engineering, Belgrade, Republic of Serbia,
e-mail: [email protected], corresponding author, w ORCID iD https://orcid.org/0009-0001-6486-5084
01 b College of Applied Studies Aviation Academy,
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O Belgrade, Republic of Serbia,
_ e-mail: [email protected],
< ORCID iD https://orcid.org/0009-0004-6735-090X : Military Technical Institute, Belgrade, Republic of Serbia,
q e-mail: [email protected],
lu ORCID iD https://orcid.org/0009-0009-2339-1933
>- d Kosovo and Metohija Academy od Applied Studies,
< Zvecan, Republic of Serbia, e-mail: [email protected] ORCID iD https://orcid.org/0000-0002-9695-7776
doi https://doi.org/10.5937/vojtehg72-50491
w FIELD: mechanical engineering
_i ARTICLE TYPE: original scientific paper
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Abstract:
Introduction/purpose: The aim of this paper is to obtain quantitative and w qualitative indicators of vehicle condition and reliability based on operational
data which can be used to determine the optimal periodicity of preventive maintenance for special purpose vehicles, and to more accurately manage the maintenance process and the operational readiness of these vehicles. Methods: Based on operational failure data and statistical methods, a mathematical model of the reliability of special purpose vehicles was determined. Using this model and operational data, the periodicity of preventive maintenance for the vehicles was determined through multi-criteria optimization, considering both readiness and minimum maintenance costs. The same methodology was applied to determine the optimal preventive maintenance periodicity for 15 components of the mechanical system of special purpose vehicles. A group analysis was conducted using Minitab 15 statistical software, based on the theory of similarity in preventive maintenance periodicity for the 15 components, and a statistical analysis of the conducted research was performed using Minitab 16 statistical software.
Results: Models for preventive maintenance of special purpose vehicles were developed based on the recorded vehicle failures and the failures of
fifteen vital components. A group analysis grouped the fifteen components g into optimal maintenance groups, similar in terms of working time between 00
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failures. The statistical analysis of the research determined a functional relationship for the optimal periodicity of preventive maintenance for special purpose vehicles.
Conclusion: The maintenance periodicity obtained through multi-criteria analysis is optimal, as it achieves satisfactory vehicle readiness with optimal maintenance costs. The statistical analysis of the research concluded that the maintenance periodicities of the vehicle components are different. In both fleets, the engine and the transmission block have the longest maintenance intervals. The research results can be used to rationalize the existing preventive maintenance concept.
Key words: vehicle, reliability, availability, preventive maintenance, costs, optimal periodicity, multi-criteria analysis.
Introduction
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Special purpose vehicles are the most common combat armored vehicles in the Serbian Armed Forces. They are used for general, special, ¡= and specific tasks, and are thus exposed to various workloads. Therefore, vehicles, their components, subassemblies, assemblies, and aggregates are exposed to constant environmental influences and disturbances that occur in processes of state changes, resulting in failures of various kinds. Such changes necessitate vehicle maintenance as a system, which should have a required relationship of permissible deviations from its prescribed o technical and operational capabilities. The key question in maintaining special purpose vehicles is primarily to avoid the consequences of failures and to return the vehicle to a defined operational state. To achieve this, it is necessary to minimize failures to an acceptable level and, if possible, prevent their occurrence entirely by implementing preventive maintenance procedures at appropriate intervals.
Given all of the above, the aim of this study is to determine, based on vehicle operation data, the quantitative and qualitative indicators of vehicle condition and reliability. These indicators are used to establish the optimal periodicity for preventive maintenance of special purpose vehicles.
Methodology
The subject of this research refers to the vehicles from two technical parks, the A fleet/park and the B fleet/park. Each fleet has 20 special purpose vehicles (referred to as the vehicles in the following text). The vehicles from both fleets were used and stored under different operational and storage conditions. The data on operating times until failure were
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collected over a period of two years. During this period, the fleet of the A park had 269 failures, and the fleet of the B park had 279 failures. The mentioned failures, in both fleets of these vehicles, related to 29 vital components.
| One of the basic prerequisites for optimizing the vehicle maintenance
system and predicting its future behavior is finding a mathematical model that can represent the behavior of vehicles in terms of fault occurrence. If it is possible to determine the reliability distribution law, it is possible to determine all reliability parameters (reliability, unreliability, failure rate, failure density, time to failure). Therefore, based on the recorded failures,
0 a reliability model of vehicles in the two fleets in question was determined.
< After grouping failures by intervals of operating times until failure, an ° assessment was performed of the following reliability indicators: reliability
function, unreliability function, failure density distribution function, and failure rate function (Catic, 2005, pp.157-232; Biocanin & Pavlovic, 2011, £ pp.106-113; Biocanin & Timotijevic, 2020, pp.1-6; Biocanin & Timotijevic,
< 2021, pp.474-480; British Standards Institution, 2024). Based on the
1 obtained data, theoretical distributions that best approximate empirical distributions were selected. The approximation of empirical distributions was done using the theoretical Weibull, exponential, Rayleigh, and normal
w distributions. The concordance of empirical and theoretical distributions ^ was assessed using Kolmogorov-Smirnov, Pearson, and Romanovsky s2 tests (Catic, 2005, pp.157-232; Biocanin & Pavlovic, 2011, pp.106-113; Biocanin & Timotijevic, 2020, pp.1-6; Biocanin & Timotijevic, 2021, pp.474480; British Standards Institution, 2024; Krstic et al, 2013). Based on the test results, for both vehicle fleets, the Weibull two-parameter distribution was adopted for the reliability model, confirming the universality of the Weibull two-parameter distribution in determining the reliability of complex ^ technical systems.
The optimal maintenance interval for the vehicles was determined based on the criterion of maximum readiness and the criterion of minimum costs. The preventive maintenance interval obtained based on the criterion of maximum readiness, where the highest allowable costs are the constraint, is shorter than the interval obtained based on the criterion of minimum costs, where the minimum level of readiness is the constraint. This means that preventive maintenance according to the maximum readiness model needs to be performed more frequently, leading to higher maintenance costs. Preventive maintenance according to the minimum costs model is performed less frequently, but the vehicle readiness is unsatisfactory. Therefore, the preventive maintenance interval for the vehicles was determined by a compromise solution that considers both
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criteria. The maintenance interval obtained in this way is optimal because it achieves satisfactory vehicle availability with optimal maintenance costs.
Following this, the maintenance interval for the vehicle components was determined by a compromise solution, i.e., one of the methods of multi-criteria optimization.
A statistical analysis of the functional relationship of the maintenance periodicity of the vehicle components was performed.
In order to further optimize and manage preventive maintenance processes more precisely based on the determined optimal maintenance intervals for components, a group analysis of the obtained maintenance intervals was performed using Minitab 15 statistical software, which resulted in optimal grouping of local maintenance intervals for multiple components into a single common interval. This yielded another model for preventive maintenance of vehicles.
Results
The periodicity of preventive maintenance for specialized vehicles
According to the established methodology (Catic, 2005, pp.158-232; British Standards Institution, 2024; Biocanin & Timotijevic, 2021; Biocanin Pavlovic, 2011, pp.106-130), based on the systematized data on the vehicle failures from the A fleet and the B fleet, a reliability model of the vehicles from both fleets was determined. Then, the optimal maintenance periodicity for the vehicles was determined based on the criterion of maximum availability (Krstic, 2009, p.488), the criterion of minimum costs (Krstic, 2009, p.488), and a compromise solution (Bass & Kwakernaak, 1977, pp.47-58; Vincke, 1992.; Paul Yoon & Hwang, 1995; Pavlicic, 2000, pp.109-122; Vargas, 1990; Biocanin & Timotijevic, 2021; Biocanin & Pavlovic, 2011, pp.106-130; Biocanin & Timotijevic, 2023, pp.1084-1092), which provides satisfactory availability of the vehicles with the optimal maintenance costs.
a) The reliability model of the vehicles from the A fleet
In order to determine a theoretical distribution model that could be used to approximate the empirical distribution, an approximation of the empirical distribution was made using the theoretical Weibull, exponential, Rayleigh, and normal distributions. The agreement between the empirical and theoretical distributions was evaluated using the Kolmogorov-Smirnov test, Pearson's test, and the Romanovsky test. Statistical data processing was performed using the Statistics Toolbox for use with MATLAB.
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Utilizing non-parametric testing of hypothetical distribution models, the quantitative indicators of deviation of theoretical models from the empirical distribution are obtained. The calculated deviations can be used not only to confirm whether the theoretical model satisfies a certain | significance level test, but also to adopt the theoretical model for which all or the majority of deviations are the smallest (Catic, 2005, pp.158-232). o The reliability function of the vehicles from the A fleet according to cc (Biocanin & Timotijevic, 2021) is as follows:
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where:
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R is the reliability function, k - Pwa is the shape parameter for the A fleet,
- qWa is the scale parameter for the A fleet, and
- t is time.
b) The reliability model of the vehicles from the B fleet
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^ According to the same methodology, a reliability model for the
s2 vehicles in the B park was determined (Table 1).
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i Based on the obtained data, the Weibull two-parameter distribution
has been adopted as the approximate reliability model with a scale parameter nwb = 385.7522 and a shape parameter pwb = 2.8800. The reliability function for the special purpose vehicles from the B park is as follows:
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where:
- R is the reliability function,
- pwb is the shape parameter for the B fleet,
- r\wb is the scale parameter for the B fleet, and
- t is time.
Table 1 - Distribution models for the B fleet
Distribution Kolmogorov-Smirnov test Pearson's test Romanovsky test Note
PA T Z/N PA T Z/N PA T Z/N
Weibull 0.0401 0.0641 Z 0.0103 12.592 Z 1.7291 3 Z Adopted distribution
Exponential 0.3013 0.0641 N 0.5531 14.067 Z 1.7230 3 Z
Rayleigh 0.1345 0.0641 N 0.1358 14.067 Z 1.8345 3 Z
Normal 0.0538 0.0641 Z 0.0146 12.592 Z 1.7278 3 Z
The reliability function for the vehicles in the B fleet / t \ßwb , t .2.8800 R(f) = e =e \3ei.9i22)
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Determination of the periodicity of preventive maintenance of the vehicles using the maximum avialibility criterion
By varying the periodicity of the time between preventive maintenance, a functional dependence of availability on maintenance periodicity is obtained, based on which the maintenance periodicity that provides maximum readiness can be determined.
The value of operational readiness (Krstic, 2009, p.488) is as follows:
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(3)
where:
- G(t) is the operational readiness,
- tr is the operating time,
- thr is the waiting time for operation availability,
- tp is the preventive maintenance time,
- F(t) is the unreliability function, and
- tk is the corrective maintenance time.
a) A fleet
The results of determining availability for different maintenance periodicities in the A fleet are given in Table 2.
In Figure 1, a graphical representation of the dependence of readiness on the periodicity of preventive maintenance of the vehicles in the A fleet is given (Krstic et al, 2013).
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According to (Biocanin & Timotijevic, 2021), based on Figure 5, it can be concluded that the maximum readiness of the vehicles from the A fleet (Gmax=0.9940) is achieved for a maintenance periodicity of tr =113 hours of work [h], because for this maintenance periodicity, the function G(t) reaches its maximum.
Table 2 - Results of determining the availability for different maintenance periodicities in
the A fleet
Maintenan
ce periodicity [h] 50 100 150 200 250 300 350 400 450 500
tr [h] 50 100 150 200 250 300 350 400 450 500
tp [h] 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0
tk [h] 90.0 90.0 90.0 90.0 90.0 90.0 90.0 90.0 90.0 90.0
F(t) 0.00 88 0.04 88 0.12 88 0.24 65 0.39 00 0.54 15 0.68 22 0.79 82 0.88 33 0.93 89
R(t) 0.99 12 0.95 12 0.87 12 0.75 35 0.61 00 0.45 85 0.31 78 0.20 18 0.11 67 0.06 11
thr [h] 1.15 0 2.30 0 3.45 0 4.60 0 5.75 0 6.90 0 8.05 0 9.20 0 10.3 50 11.5 00
G(t) 0.99 10 0.99 39 0.99 35 0.99 18 0.98 88 0.98 41 0.97 63 0.96 32 0.93 985 0.89 59
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Figure 1 - Dependency of readiness on the periodicity of preventive maintenance of the special purpose vehicles in the A fleet
Figure 2 - Dependency of readiness on the periodicity of preventive maintenance of the special purpose vehicles in the B fleet
b) B fleet
Using the same methodology as for the A fleet, the optimal maintenance periodicity for the vehicles in the B fleet has been determined. Table 3 shows the results of the availability determination for different maintenance frequencies in the B fleet.
Table 3 - Results of determining the availability for different maintenance periodicities in
the B fleet
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Maintenance periodicity [h] 50 100 150 200 250 300 350 400 450 500
tr [h] 50 100 150 200 250 300 350 400 450 500
tp [h] 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0
tk [h] 90.0 90.0 90.0 90.0 90.0 90.0 90.0 90.0 90.0 90.0
F(t) 0.0028 0.0203 0.0637 0.1400 0.2493 0.3842 0.5303 0.6705 0.7895 0.8789
R(t) 0.9972 0.9797 0.9363 0.8600 0.7507 0.6158 0.4697 0.3295 0.2105 0.1211
thr [h] 1.150 2.300 3.450 4.600 5.750 6.900 8.050 9.200 10.350 11.500
G(t) 0.9915 0.9950 0.99554 0.9948 0.9934 0.9909 0.9868 0.9802 0.9688 0.9476
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Based on Figure 2, it can be concluded that the maximum readiness of the vehicles from the B fleet (Gmax = 0.9956) is achieved for a maintenance periodicity of tr = 141 h.
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Determination of the periodicity of preventive maintenance of vehicles using the minimum cost criterion
This model determines the optimal interval for the periodic implementation of preventive maintenance procedures for vehicles, which minimizes costs while ensuring the required availability and readiness.
Maintenance costs can be expressed as (Krstic, 2009, p.488; Krstic et al, 2013):
Ck-(Çk-Cp}R(t)
C(t) =
R(t)dt
(4)
where:
- C(t) is maintenance costs,
- Ck is corrective maintenance costs, and
- Cp is preventive maintenance costs.
a) A fleet
By applying the given expression for maintenance costs, for different periodicities of preventive maintenance of special purpose vehicles, the maintenance cost values for the vehicles in the A fleet were obtained, as shown in Table 4.
Table 4 - Maintenance costs for different maintenance periodicities of the vehicles in the
A fleet
Maintenance periodicity [h] 50 100 150 200 250 300 350 400 450 500
Ck [mu] 35000 35000 35000 35000 35000 35000 35000 35000 35000 35000
Cp [mu] 7000 7000 7000 7000 7000 7000 7000 7000 7000 7000
R(t) 0.9912 0.9512 0.8712 0.7535 0.6100 0.4585 0.3178 0.2018 0.1167 0.0611
1 R(t)dt -'o 49.87 98.590 144.31 185.07 219.23 245.93 265.26 278.13 285.96 290.30
C(t) [mu] 145.30 84.86 73.49 75.11 81.74 90.11 98.39 105.52 110.,96 114.67
Note: The abbreviation "mu" in Table 4 reffers to a "monetary unit".
The data on preventive maintenance costs (Cp) and corrective maintenance costs (Ck) are taken from the accounting documentation of the authorized maintenance workshop.
Table 2 shows that the minimum maintenance costs for the vehicles in the A fleet are achieved for a preventive maintenance periodicity of 100 to 200 h. By discretizing the maintenance periodicity interval from 50 to 500 with a step of 1, the minimum total specific costs and the maintenance periodicity for minimum total specific costs are calculated.
Figure 3 provides a graphical representation of the dependence of total specific costs on the periodicity of preventive maintenance of vehicles.
According to (Krstic et al, 2013) and Figure 3, the optimal maintenance periodicity is 163 h.
Figure 3 - Dependency of total specific costs on the periodicity of preventive maintenance of the special purpose vehicles in the A fleet
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Figure 4 - Dependency of total specific costs on the periodicity of preventive maintenance of the special purpose vehicles in the B fleet
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b) B fleet
Using the same methodology as for the A fleet, the maintenance costs (Table 5) and the optimal maintenance periodicity for the vehicles in the B fleet were determined (Figure 4). Based on Figure 4, it can be concluded that the minimum costs (Cmn=56.6405 [mu]) are achieved for a maintenance periodicity of the vehicles from the B fleet of tr = 193 h.
Table 5 - Maintenance costs for different maintenance periodicities of the vehicles in the
B fleet
Maintenance periodicity [h] 50 100 150 200 250 300 350 400 450 500
Ck [mu] 35000 35000 35000 35000 35000 35000 35000 35000 35000 35000
Cp [mu] 7000 7000 7000 7000 7000 7000 7000 7000 7000 7000
R(t) 0.9972 0.9797 0.9363 0.8600 0.7507 0.6158 0.4697 0.3295 0.2105 0.1211
i R(f)dt Jo 49.964 99.475 147.50 192.55 232.94 267.19 294.34 314.26 327.64 335.80
Ct [mu] 141.658 76.077 59.555 56.709 60.015 66.456 74.230 82.012 88.835 94.126
Determination of the optimal periodicity of preventive maintenance of vehicles using multi-criteria optimization
For solving this task, a multi-criteria optimization method known in the literature as MCDM (Multi Criteria Decision Making) was applied. One of the methods of multi-criteria optimization used in the paper is the Analytic Hierarchy Process (AHP). This method is based on the principle of decision making based on the knowledge and data available at the time of decision making. The creative decision-making process is scientifically based on the concept of analytics, hierarchy, and process, as well as on the benefit and the cost criteria of optimality.
a) A fleet
The optimal periodicity of preventive maintenance has been determined and amounts to 162 h (Biocanin & Timotijevic, 2021).
b) B fleet
Using the same methodology, the optimal periodicity of preventive maintenance for the special purpose vehicles in the B fleet was adopted and amounts to 192 h. Figure 6 provides a graphical representation of finding the best alternative.
Figure 5 - Graphic representation of finding a compromise solution for the A fleet
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Figure 6 - Graphic representation of alternatives for determining the maintenance periodicity by a compromise solution in the B fleet
The value of the optimal periodicity for conducting preventive maintenance procedures for special purpose vehicles is determined according to the criterion of the maximum vehicle readiness to be 113 h
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for the vehicles from the A fleet and 141 h for the vehicles from the B fleet, and according to the criterion of the minimum maintenance costs to be 163 h for the vehicles from the A fleet and 193 h for the vehicles from the B fleet. By compromising, the value of the sought optimal periodicity for conducting preventive maintenance procedures was determined, taking into account both optimization criteria, and it amounts to 162 h for the vehicles from the A fleet and 192 h for the vehicles from the B fleet. A graphical representation of the periodicity of preventive maintenance for special purpose vehicles, determined according to the three mentioned criteria, is given in Figure 7 for the vehicles from the A fleet and in Figure 8 for the vehicles from the B fleet.
Figure 7 -
Graphic representation of the preventive maintenance period for the specialized vehicles in the A park
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Figure 8 - Graphic representation of the preventive maintenance period for the specialized vehicles in the B park
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The calculated periodicities of preventive maintenance for the special g
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purpose vehicles in the A fleet and the B fleet are different. The maintenance periodicity for the vehicles in the B fleet is longer than that for the vehicles in the A fleet.
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The vehicles from B fleet were used under harsher operational conditions in a geographic area with notably hilly terrain. The driving crews and maintenance personnel are less trained, and the workshop capacities are inadequate for work, especially during the winter period, with workshop § equipment being of an older generation. The storage and preservation conditions for the vehicles are also worse since vehicles in the A fleet are stored under a canopy, while those in the B fleet are stored in an open
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Determination of the optimal periodicity of preventive maintenance of vehicles by analyzing the maintenance periodicity of vehicle components
Most models optimize preventive maintenance of technical systems at the component level. Therefore, it is a major challenge to optimize the preventive maintenance process of a complex technical system such as a motor vehicle, which consists of several tens of thousands of components, 50-60% of which lose their initial properties during operation, 30-40% have a shorter lifespan than the vehicle, and an average of 200-300 -g components are critical in terms of reliability and require more frequent | preventive inspections and corrective actions. The solution lies in finding an optimization model for preventive maintenance of vehicles based on the combined application of the results of several maintenance mathematical models for vehicle components, and then grouping the preventive maintenance periods for multiple components into a common ete periodicity. To avoid frequent vehicle downtimes for preventive o maintenance, optimal grouping of local periodicities for multiple t al components into a common periodicity was performed through a group e analysis. This approach enables more precise management of vehicle maintenance processes and their operational readiness, which was the a goal of this study. '8
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Using the same methodology as in the previous section of this study, a reliability model and optimal periodicity of preventive maintenance were determined for fifteen vital components of the vehicles.
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The optimal periodicity of preventive maintenance for the vital components of the vehicles in the A fleet
The results of the research on the optimal periodicity of preventive maintenance for vital components of vehicles according to the three models are presented in the following Table 7.
Table 7 - Optimal maintenance periodicities for the components of the specialized
vehicles in the A fleet
No. Component name Criterion Maintenance Periodicity Time (h)
1 2 3 4
1. Transmission Unit Block (TUB) Maximum Availability 115
Minimum Costs 341
Compromise Solution 332
2. Hydraulic System for Fan Drive (HCPF) Maximum Availability 105
Minimum Costs 183
Compromise Solution 172
3. Coolant Heating Device for Engine and Transmission Maximum Availability 109
Minimum Costs 169
Compromise Solution 181
4. Control Block Maximum Availability 144
Minimum Costs 199
Compromise Solution 162
5. Side Transmission with Disk Brakes Maximum Availability 127
Minimum Costs 161
Compromise Solution 157
6. Hand Brake with Command Maximum Availability 174
Minimum Costs 113
Compromise Solution 164
7. Water Ingress Protection Mechanism for the Engine Maximum Availability 123
Minimum Costs 194
Compromise Solution 181
8. Engine Throttle Controls Maximum Availability 117
Minimum Costs 201
Compromise Solution 173
9. Armored Body (connecting parts) Maximum Availability 139
Minimum Costs 175
Compromise Solution 194
10. Commander's Turret with links to the Armored Body Maximum Availability 129
Minimum Costs 187
Compromise Solution 169
No.
Component name
Criterion
Maintenance Periodicity Time (h)
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Maximum Availability
117
Cooler Louver and Fan Controls
Minimum Costs
176
Compromise Solution
171
12.
Windbreak with Movement Mechanism
Maximum Availability
147
Minimum Costs
184
Compromise Solution
173
13.
Maximum Availability
154
Fire Prevention Device
Minimum Costs
191
Compromise Solution
181
14.
Maximum Availability
112
Barrel Gas Vent Device
Minimum Costs
184
Compromise Solution
163
1 5.
Maximum Availability
142
Engine OM-403
Minimum Costs
372
Compromise Solution
362
The optimal periodicity of preventive maintenance for the vital components of the vehicles in the B fleet
Using the same methodology as for the vital components of the vehicles in the A fleet, the periodicities of preventive maintenance for the components in the B fleet were calculated and provided in Table 8.
Table 8 - Optimal maintenance periodicities for the components of the specialized
vehicles in the B fleet
No.
Component name
Criterion
Maintenance Periodicity Time (h)
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Maximum Availability
140
Transmission Unit Block (TUB)
Minimum Costs
352
Compromise Solution
348
Hydraulic System for Fan Drive (HCPF)
Maximum Availability
112
Minimum Costs
194
Compromise Solution
186
Coolant Heating Device for Engine and Transmission
Maximum Availability
116
Minimum Costs
187
Compromise Solution
182
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No. Component name Criterion Maintenance Periodicity Time (h)
1 2 3 4
4. Control Block Maximum Availability 152
Minimum Costs 204
Compromise Solution 195
5. Side Transmission with Disk Brakes Maximum Availability 141
Minimum Costs 192
Compromise Solution 184
6. Hand Brake with Command Maximum Availability 133
Minimum Costs 188
Compromise Solution 186
7. Water Ingress Protection Mechanism for the Engine Maximum Availability 136
Minimum Costs 215
Compromise Solution 203
8. Engine Throttle Controls Maximum Availability 121
Minimum Costs 204
Compromise Solution 198
9. Armored Body (connecting parts) Maximum Availability 140
Minimum Costs 202
Compromise Solution 194
10. Commander's Turret with links to the Armored Body Maximum Availability 161
Minimum Costs 193
Compromise Solution 183
11. Cooler Louver and Fan Controls Maximum Availability 137
Minimum Costs 199
Compromise Solution 187
12. Windbreak with Movement Mechanism Maximum Availability 162
Minimum Costs 201
Compromise Solution 190
13. Fire Prevention Device Maximum Availability 157
Minimum Costs 218
Compromise Solution 212
14. Barrel Gas Vent Device Maximum Availability 141
Minimum Costs 201
Compromise Solution 191
15. Engine OM-403 Maximum Availability 152
Minimum Costs 369
Compromise Solution 361
Based on the obtained results, it can be concluded that there are g different maintenance periodicities for the components, and that in both 00 fleets, the transmission unit and the OM 403 engine of special purpose
vehicles have the longest maintenance periodicity. Cluster/group analysis
A cluster analysis was performed based on the theory of similarity of the adopted compromise solution for the optimal maintenance periodicity of 15 vehicle components.
The analysis was carried out using Minitab 15statistical software, based on Jaccard similarity coefficients:
where:
- A and B are the lengths of the optimal maintenance periodicity for two consecutive components/assemblies compared.
The similarity coefficient matrix for the A fleet is shown in Table 9, and
Table 11 displays the results of grouping the optimal maintenance periodicities of components into two, three, four, five, and six groups for the A fleet and the B fleet.
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similarity coefficient = ^^ (5) ™
J AUB V ' B
_0
that for the B fleet in Table 10. e
Within each group, the components are simultaneously subjected to q preventive maintenance with adopted optimal maintenance periodicities.
0
Depending on the number of optimal maintenance periodicities, a vehicle will enter the workshop many times for the purpose of preventive maintenance. io
m
For the second group, the vehicle will enter the workshop twice, and for the sixth group, six times.
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Table 9 - Jaccard similarity coefficients for the A fleet
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o o o o o CI o O o CD CD o CD o
. 1. IJJ s s s s s rsl -» a» i) 1» i a CN Hi S § a -r
o o o o o o o o o CD o CD CD o
m S K CI» 1 i r» s § 1 K a» r— s S CI» 3 S Cll i i §
o o o o o o o CD CD CD o CD
N rx s a s s K g Cll s R 3 a CM CO r-
o o o o o o o o O o o CD o
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o o o o o o o o o CD — o o o O
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o o o o o o o o CD CD o o CD CD
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o o o o o O o CD CD CD CD CD CD CD
-i- 3 rsl -t t* CO 1 i i CO s 1» 1 a Oi -» a» Si Cb " I CO s T
CD o CD CD CD CD CD CD CD CD CD CD CD CD
co IT) u. in <>• 1 in 11. CO s § | K O0 CJi s "f I-I <>. in a> a o> | i §
o O O o O C3 CD CD CD CD CD CD
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° ° o ° ° ° ° CD CD ° ° CD
m « m
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o o o o o o o o CD CD CD o o CD
- fN cO in CO - CO a> O = CN co : m
3ivo3aoov/dnodG Aisw3ssv
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Table 10 - Jaccard similarity coefficients for the B fleet
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Table 11 — Grouping of components/aggregates for simultaneous preventive
maintenance
Group A fleet B fleet
Agregate Xi; i=1,2,3.....15 Opt. period. Min Xi /hour/ Agregate Xi; i=1,2,3.....15 Opt. period. Min Xi /hour/
II group 1, 15 332 1, 14,15 191
2,3,4,5,6,7,8,9,10,11,12,13,14 157 2,3,4,5,6,7,8,9,10,11,12,13 182
III group 1,15 332 1,15 348
3,7,13 181 4,7,8,13 195
2, 4, 5, 6, 8, 9,10,11,12,14 157 2,3, 5,6, 9,10,11,12,14 182
IV group 1,15 332 1 348
2,3,7,8,9,11,12,13 171 2,3,4,5,6,7,8,9,10,11,12,13 182
4,5,6,14 157 14 191
10 169 15 351
V group 1,15 332 1 348
2,8,9,11,12 171 2,3,4,5,6,7,8,9,10,11,12 182
3,7,13 181 13 212
4,5,6 157 14 191
10 169 15 351
VI group 1 332 1 348
2,8,9,11,12 171 2,3,4,5,6,7,8,9,11,12 182
3,7,13 181 10 183
4,5,6,14 157 13 212
10 169 14 191
15 362 15 351
An example dendrogram for grouping parts into three groups is shown in Figure 9.
An example dendrogram for grouping parts into five groups is shown in Figure 10.
Based on the obtained results, the following preventive maintenance model for components has been selected:
A fleet: The optimal periodicity for preventive maintenance for the engine and transmission in the specialized vehicle unit block is 332 h, while it is 157 h for all other components. In order to reduce maintenance costs, the engine and transmission in the unit block should undergo preventive maintenance every other time the vehicle enters the workshop.
B fleet: The optimal periodicity for preventive maintenance for the engine and transmission in the specialized vehicle unit block is 348 h, while it is 182 h for all other components.
Dendrogram with Average Linkage and Correlation Coefficient Distance
1,94
„ 1,30'
0,65'
0,00'
1-1-1 I . I-1 . I-1 . I-1-
1 15 2 8 11 12 9 3 7 13 4 14 6 5 10 Variables
Figure 9 - Example of software grouping of components/aggregates into three groups Cluster 1: 1 15; Cluster 2: 2 3 4 5 6 7 8 9 11 12 13 14; Cluster 3: 10
Dendrogram with Average Linkage and Correlation Coefficient Distance
1,45-
0,97-
0,48-
0,00-"—-1--1
1 14 15 2
,-1-1-1 r*
5 12 11 4 9 Variables
-i-1-
7 10 13
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Figure 10 - Example of software grouping of components/aggregates into five groups Cluster 1: 1 15; Cluster 2: 2 8 9 11 12; Cluster 3: 3 7 13; Cluster 4: 4 5 6 14; Cluster 5: 10
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Statistical analysis of the research
Based on the analysis of the periodicity of preventive maintenance of vital vehicle components, using the same methodology, it was concluded that the maintenance periodicities of these components are different. In order to investigate the functional relationship of optimal periodicities of g preventive maintenance of vehicles, an experiment was conducted in Minitab 16 statistical software based on 5 measurements of time between Eu two failures for the observed 15 groups of components, in the A fleet and § the B fleet.
o The General full factorial design with two factors was applied:
_i 1. Factor A: Relating to the specialized vehicle fleet, with two levels
o (A fleet and B fleet).
| 2. Factor B: Relating to the components/aggregates of the specialized
ft vehicles, with 15 levels (components from 1, 2, 3, ..., 14, 15).
The X0 hypothesis was set, assuming that the mean times between k failures in the A fleet and the B fleet are equal, i.e., ^ with an alternative hypothesis X-i:
Similarly, for the component groups "system/aggregate groups," the mean times between failures were observed, with Xo =^2) and X1( ^ ^2) hypotheses. < After entering the data, certain statistical characteristics of the failure
^ times behavior were obtained depending on the FLEET and the observed ,0 COMPONENT.
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Multilevel Factorial Design
Factors: 2 Replicates: 5
Base runs: 30 Total runs: 150
Base blocks: 1 Total blocks: 1
Number of levels: 2. 15
General Linear Model: TIME BETW. FAILURE versus FLEET. SYSTEM GRUPS/AG
Factor Type Levels Values
FLEET fixed 2 1. 2
SYSTEM GRUPS/AGREGATES fixed 15 1. 2. 3. 4. 5. 6. 7. 8. 9.
10. 11. 12.
13. 14. 15
Analysis of Variance for TIME BETW. FAILURE, using Adjusted SS for Tests
Source DF Seq SS Adj SS Adj MS F
P
FLEET 1 100259 100259 100259 6,16
0,014
SYSTEM GRUPS/AGREGATES 0,044
FLEET*SYSTEM GRUPS/AGREGATES 0,991
Error Total
14 14
360803 72137
360803 72137
120 1954597 1954597 149 2487795
25772 2,58 5153 0,32 16288
Based on the given analysis of variance and based on the F-statistic and p-value for factor A, it can be concluded that Fkr > Ftabl. (6.16 > 2.76) and p < a (0.014 < 0.05), which means that the fleet, as a factor of observation, has a different effect on failures, thus rejecting the null hypothesis and accepting the alternative hypothesis.
Based on the given analysis of variance and based on the F-statistic and p-value for factor B, it was concluded that Fkr > Ftabl. (2.58 > 1.80) and p < a (0.044 < 0.05), which means that the fleet, as a factor of observation, has a different effect on failures, thus rejecting the null hypothesis and accepting the alternative hypothesis.
In addition, the coefficients R-Cq = 21.43%, R-Cq(adj) = 2.45% indicate that there is no strong correlation between the observed levels of this factor. The regression equation of the time between failures depending on the fleet is:
TIME BETWEEN FAILURES = 227.3 + 51.71 FLEET (6) and there is no polynomial relationship.
The functional relationships between the failure times of the vehicles in the A and B fleets is given in Figure 11, and the groups of components are shown in Figures 12 and 13.
Figure 11 - Functional relationship Time between failure - Park/fleet
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Figure 12 - Functional relationship Time between failure - System/Aggregate groups
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Figure 13 - Functional relationship Time between failure - System/Aggregate groups in
the polynomial relationship
A spatial view of the functional relationship of time between failures for fleets and system/aggregate groups is provided in Figure 14, a contour view in Figure 15, and the main effects in relationships and time between failures in Figures 16 and 17.
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Figure 14 - Spatial view of the functional relationship Time between failure - Fleet -System/Aggregate groups
Figure 15 - Contour view of the functional relationship Time between failure - Fleet -
System/Aggregate groups
The regression equation for the function of time between failures depending on a system/aggregate group is:
cyCTPM OP
TIME BETWEEN FAILURES = 264.8 + 5.012 GROUP (7)
AGGREGATE v '
and the polynomial relationship is:
TIME BETWEEN FAILURES = 302.8 -
8.41 SY_SJE_M GROUP + 0.8392 srJHt GROUP ** 2 (8)
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Main Effects Plot (data means) for Time between failures
GRUPE SISTEMA/ACREGATA PARK
Figure 16 - Main effects in relations Time between failures - Fleet - System/Aggregate
groups
Figure 16 clearly shows that there is a polynomial relationship between the maintenance periodicity of fifteen vital components. There is no polynomial relationship between the maintenance periodicity of the vital components in the two specialized vehicle parks.
Figure 17 - Main interactions in relations Time between failures - Fleet -System/Aggregate groups
From Figure 17, it can be seen that, by analyzing five times between failures in Minitab 16 statistical software, different maintenance periodicities for vehicles were obtained in fleet 1 (A fleet) and fleet 2 (B
fleet). Approximately the same maintenance periodicity in both fleets is g
observed for the Coolant Heating Device for the Engine and Transmission ® (Component 3), the Control Block (Component 4), and the Water Ingress S Protection Mechanism for the Engine (Component 7).
Discussion
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Stemming from the data from vehicle operation, the quantitative and qualitative indicators of the system condition and reliability of the special purpose vehicles were obtained. Based on it, the optimal maintenance periodicity of these vehicles was determined.
The periodicity of preventive maintenance of the special purpose vehicles was determined by a compromise solution. The maintenance S. periodicity obtained in this way is optimal because it provides satisfactory vehicle readiness and availability with optimal maintenance costs.
According to the research results, the maintenance periodicity of the vehicles from the B fleet is higher compared to the maintenance periodicity of the vehicles from the A fleet. |
Compared to the existing maintenance concept, the calculated m periodicity of preventive maintenance for the entire vehicle falls between the first and second technical inspections of the special purpose vehicle. It is concluded that it is unnecessary to implement preventive maintenance with two technical inspections in a relatively short time interval (first and second technical inspection); nevertheless, only one preventive inspection at the calculated time of preventive maintenance is sufficient. This approach would rationalize the existing concept of preventive maintenance, reduce maintenance costs, and increase the availability of special purpose vehicles.
Based on the analysis of the periodicity of preventive maintenance of vital components of special purpose vehicles, it was concluded, using the same methodology, that the maintenance periodicities of these components are different. The analysis and calculations showed that the ™ engine of the vehicle and the transmission, in both fleets, have the highest reliability, which is why their maintenance periodicity is significantly higher than the maintenance periodicity of other components. If preventive ra maintenance of special purpose vehicles is performed through o maintenance of components, in the calculated periods, then the special B purpose vehicle would be very often undergoing preventive maintenance, the same operations would be repeated several times (opening documentation, vehicle washing, component construction and installation, testing, etc.), which would increase costs and decrease readiness due to the long time spent on maintenance. In order to avoid this, optimal
0
c
0
grouping of local maintenance periodicities for multiple components into a single common periodicity was achieved through the group analysis. This way, the optimal availability and maintenance costs are achieved. Additionally, the group analysis showed that in the processes of vehicle I management and maintenance, operational readiness can be more precisely managed based on the determined optimal periods of preventive o maintenance.
c¿ Through the statistical analysis in Minitab 15 and Minitab 16 statistical
^ software, it was concluded that there is no polynomial relationship between g the time of operation until failure for the A fleet and the B fleet. It was
0 concluded that there is a polynomial relationship between operating time
< and failure in the analysis of the groups of vital components. By analyzing ° the five times between two failures in Minitab 16 statistical software, g different maintenance periodicities for the vehicles in park 1 (A fleet) and £ park 2 (B fleet) were obtained. The Coolant System Heating Device £ (Component 3), the Control Block (Component 4), and the Water Ingress
< Protection Mechanism for the Engine (Component 7) have approximately the same maintenance periodicity in both fleets. Other components have different maintenance periodicities, indicating that the maintenance periodicity depends on the terrain configuration and other vehicle
w operation conditions and that if the same vehicle changes its location and ^ maintenance place, optimal maintenance periodicity calculations must be s2 re-performed.
^ By grouping certain components and assemblies based on similar
1 operating times between two failures, a kind of analysis of the condition of these elements was performed at a qualitative and quantitative level. The group analysis has contributed to more precise management of operational readiness and optimal maintenance costs of vehicles in the
q processes of vehicle management and maintenance based on the determined optimal periods of preventive maintenance of components. Another preventive maintenance model for vehicles was set up through the group analysis.
The research conducted in the A fleet and the B fleet shows that the condition and the maintenance periodicity of special purpose vehicles depend on the terrain where the vehicle is used, the drivers who operate the vehicle, the crew in the vehicle, and the workshop conditions for maintenance (technical equipment and quality of personnel). The study has proven that the vehicles from the B fleet have longer maintenance intervals, indicating that the operating conditions are different. The conclusion is that if the same vehicle changes its location and maintenance place, then optimal maintenance periodicity calculations
LU
must be re-performed. The analysis also showed that the engine and the transmission in the block have high reliability and are not affected by changes in operating conditions.
Conclusion
Based on the overall results obtained in this study, it can be concluded that the most suitable preventive maintenance concept for special purpose vehicles is maintenance based on time intervals derived from reliability with preventive inspections and preventive replacements according to the periodicities obtained from the calculations in this study, with the application of functional diagnostics and the lean maintenance concept. It is necessary to continuously monitor the behavior of vehicles and the occurrence of failures in operation, which can best be done by introducing a designed information system. By analyzing failure occurrence data, it is possible to predict future reliability, which in turn allows for informed decisions on preventive inspection procedures and the replacement of parts before they fail, ultimately enhancing vehicle reliability and minimizing the impact of potential failures.
The scientific contribution of the conducted research includes:
1. Optimization of Maintenance Periodicity:
- Quantitative and qualitative indicators of the system condition and vehicle reliability were obtained based on operational data, which led to the determination of the optimal maintenance periodicity for special purpose vehicles.
- This periodicity, derived as a compromise solution, ensures satisfactory vehicle readiness with optimal maintenance costs.
2. Improvement in Maintenance Strategies:
- The calculated periodicity for preventive maintenance falls between the first and second technical inspections, suggesting the redundancy of two closely timed technical inspections. Instead, a single preventive inspection at the calculated time would suffice, rationalizing the existing maintenance concept, reducing costs, and enhancing vehicle readiness.
3. Analysis of Vital Assemblies:
- The analysis revealed different maintenance periodicities for vital assemblies. Engines and transmissions in both parks showed the highest reliability, resulting in significantly longer maintenance intervals compared to other assemblies.
- Optimal grouping of local periodicities for multiple assemblies into a common periodicity was performed through group analysis, achieving optimal readiness and maintenance costs.
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0 si "o "0
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^ 4. Statistical Analysis Findings:
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Statistical analysis using Minitab 15 and 16 concluded that there is no polynomial relationship between operation time until failure for the A and B parks, but such a relationship exists for the group of fifteen
° vital assemblies.
Different maintenance periodicities were determined for the vehicles in the A and B parks. Some assemblies, such as the engine cooling system and transmission (assembly 3), the steering block (assembly 4), and the water protection mechanism (assembly 7), had similar maintenance periodicities in both parks. o 5. Influence of Operational Conditions:
< - The research showed that the condition and the maintenance
° periodicity of special purpose vehicles depend on the terrain, drivers,
X
crew, and workshop conditions.
- The vehicles in the B park had longer times between maintenance, £ indicating different operational conditions. If a vehicle changes < location and a maintenance site, new calculations for optimal
maintenance periodicity must be performed.
6. Development of a New Preventive Maintenance Model:
- Grouping certain assemblies and aggregates based on similar time w between failures allowed for a comprehensive analysis of their ^ condition on a qualitative and quantitative level.
s2 - The group analysis contributed to more precise management of
^ vehicle operational readiness with optimal maintenance costs based
NEH on determined optimal preventive maintenance periods.
7. Adaptation to Different Operational Conditions:
- The analysis indicated that maintenance periodicity depends on terrain configuration and other operational conditions. If the same
^ vehicle changes the location, optimal maintenance periodicity
calculations need to be recalculated.
- The study also found that the engine and the transmission block have high reliability and are not significantly affected by changes in operational conditions.
The results in this paper represent a research contribution to determining the optimal periodicity of preventive maintenance for special purpose vehicles and can serve as a basis for further research. The continuation of the research could proceed in the following directions: • Using the presented methodology, or a similar one, to investigate the periodicity of preventive maintenance for other assemblies of
special purpose vehicles (such as armaments and communication g equipment);
Conduct research on the optimal periodicity of preventive S> maintenance for all other fleets of special-purpose equipment using the presented methodology, with the aim of making final and precise conclusions on the optimal periodicity of maintenance for special purpose vehicles;
Investigate the maintenance periodicity of vehicles in passive operation that are exposed to various degradative factors under § such conditions; and
Explore the possibility of installing an automated diagnostic system for functional diagnostics to ensure objective, highly precise, and continuous monitoring of the vehicle's operation and its vital systems (e.g., oil pressure drop in the engine, insufficient brake fluid level, excessive wear of brake pads, excessive wear of clutch disc linings, clogged fuel filters, failure of missile launch and guidance systems, wear of the drive wheel gear, wear of the support wheel, etc.). e
References
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in a> o
0 >
0
cp
ra o 0 cp tn
Bass, S.M. & Kwakernaak, H. 1977. Rating and ranking of multiple-aspect alternatives using fuzzy sets. Automatics, 13(1), pp.47-58. Available at: -g https://doi.org/10.1016/0005-1098(77)90008-5. |
Biocanin, S. & Pavlovic, M. 2011. Determining the optimal periodicity of the V46-6 engine preventive maintenance. Vojnotehnicki glasnik/Military Technical, 59(3), pp.106-130 (in Serbian). Available at:
https://doi.org/10.5937/vojtehg1103106B.
Biocanin, S. & Timotijevic M. 2020. A reliability analysis of the horizontal milling machine: GVK-1P. IMK-14 - Istrazivanje i razvoj, 26(1), pp.1-6 (in Serbian). Available at: https://doi.org/10.5937/IMK2001001B. o
Biocanin, S. & Timotijevic, M. 2021. Odredivanje modela pouzdanosti vozila ™ posebne namene. In: Sesti naucno-strucni skup Politehnika sa medunarodnim uCescem, Belgrade, Serbia, pp.474-480, December 10 [online]. Available at: https://conference.politehnika.edu.rs/ [Accessed: 15.04.2024].
Biocanin, S. & Timotijevic, M. 2023. Analysis of research on optimization .8 models and algorithms for planning preventive maintenance of machine systems. ¡§ In: International scientific and professional conference Politehnika 2023, Belgrade, Serbia, pp.1084-1092, December 15 [online]. Available at: https://conference.politehnika.edu.rs/ [Accessed: 15.04.2024].
-British Standards Institution (BSI). 2024. Multi-part Document BS 5760 -Reliability of systems, equipment and components. Available at: https://doi.org/10.3403/BS5760.
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^ Catic, D. 2005. Razvoj i primena metoda teorija pouzdanosti. Monografija.
| Kragujevac: University of Kragujevac, Faculty of Mechanical Engineering (in w Serbian). ISBN: 86-80581-80-1.
Krstic, B. 2009. Tehnicka eksploatacija motornih vozila i motora. Kragujevac, Serbia: University of Kragujevac, Faculty of Engineering (in Serbian). > ISBN: 9788690181919. 3 Krstic, B., Nikolic, R., Biocanin, S., Nikolic, I., Krstic, I. & Krstic, V. 2013.
° Determination of the Driving Engine Reliability for the Special Purposes Motor cL vehicle. In: 10-th European Conference of Young Researchers and Scientists, Zilina, Slovak Republic, Section 2, June 24-26.
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^ https://scindeks.ceon.rs/article.aspx?artid=0354-02430001109P [Accessed: 10
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Pavlicic, D. 2000. Normalization of attribute values in MADM violates the
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ft Paul Yoon, K. & Hwang, C.L. 1995. Multiple Attribute Decision Making: An
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0
Determinación de un modelo de mantenimiento preventivo de vehículos de propósito especial
Stojko Lj. Biocanina, Milica S. Timotijevicb, x Zeljko M. Bulatovicc, Milan A. Misicd
LU
a Academia de Estudios Técnicos Aplicados de Belgrado, Departamento de Ingeniería Informática y Mecánica, Belgrado, República de Serbia, autor de correspondencia
b Academia de Aviación de la Facultad de Estudios Aplicados, Belgrado, República de Serbia
c Instituto Técnico Militar, Belgrado, República de Serbia d Academia de Estudios Aplicados de Kosovo y Metohija, Zvecan, República de Serbia
CAMPO: ingeniería mecánica TIPO DE ARTÍCULO: artículo científico original
Resumen:
Introducción/objetivo: El objetivo de este documento es obtener indicadores cuantitativos y cualitativos del estado y la confiabilidad del vehículo basados en datos operativos que puedan usarse para determinar la periodicidad óptima del mantenimiento preventivo para vehículos de
propósito especial y para gestionar con mayor precisión el proceso de °° mantenimiento y la disponibilidad operativa de estos vehículos. 00
Métodos: A partir de datos de fallas operativas y métodos estadísticos, se ñ
determinó un modelo matemático de confiabilidad de vehículos para fines especiales. Utilizando este modelo y datos operativos, se determinó la periodicidad del mantenimiento preventivo de los vehículos mediante optimización multicriterio, considerando tanto la disponibilidad como los costos mínimos de mantenimiento. La misma metodología se aplicó para determinarla periodicidad óptima de mantenimiento preventivo de 15 componentes del sistema mecánico de vehículos de propósito especial. Se realizó un análisis grupal mediante el software estadístico Minitab 15, basado en la teoría de la similitud en la periodicidad del mantenimiento preventivo para los 15 componentes, y un análisis estadístico de la investigación realizada mediante el software estadístico Minitab 16. Resultados: Se desarrollaron modelos para el mantenimiento preventivo de vehículos de propósito especial con base en las fallas registradas de los vehículos y las fallas de quince componentes vitales. Un análisis de grupo reunió los quince componentes en grupos de mantenimiento óptimos, similares en términos de tiempo de trabajo entre fallas. El análisis | estadístico de la investigación determinó una relación funcional para la periodicidad óptima del mantenimiento preventivo para vehículos de propósito especial.
Conclusión: La periodicidad de mantenimiento obtenida a través del análisis multicriterio es óptima, ya que logra una disponibilidad satisfactoria
1865
ü (Л
J5
o ф
> ф
(Я
o
Ф
del vehículo con costos de mantenimiento óptimos. El análisis estadístico
o
de la investigación concluyó que las periodicidades de mantenimiento de los componentes del vehículo son diferentes. En ambas flotas, el motor y el bloque de transmisión tienen los intervalos de mantenimiento más largos. Los resultados de la investigación pueden utilizarse para racionalizar el concepto de mantenimiento preventivo existente.
Palabras claves: vehículo, confiabilidad, disponibilidad, mantenimiento
я
preventivo, costos, periodicidad óptima, análisis multicriterio.
Определение модели профилактического обслуживания автомобиля специального назначения
Стойко Л. Биочанина, Милица С. Тимотиевич6,
Желько M. Булатовичв, Милан A. Мишичг >8
о
Академия прикладных технических исследований в Белграде, in
факультет компьютерной инженерии и машиностроения, г. Белград, Республика Сербия, корреспондент 6 Авиационная академия, г. Белград, Республика Сербия в Военно-технический институт, г. Белград, Республика Сербия г Академия профессиональных исследований Косово и Метохия, г. Звечан, Республика Сербия
РУБРИКА ГРНТИ: 78.25.09 Военная автомобильная техника,
73.31.41 Техническая эксплуатация и ремонт средств
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0
ВИД СТАТЬИ: оригинальная научная статья Резюме:
ш з
ю автомобильного транспорта. Автосервис
2,
7
Введение/цель: Целью данной статьи является получение о количественных и качественных показателей состояния и
™ надежности транспортных средств на основании
ш эксплуатационных данных, которые могут быть использованы
о профилактического обслуживания транспортных средств
° специального назначения и для более точного управления
^ для определения оптимальной периодичности
о
о _|
^ процессом технического обслуживания и эксплуатационной
готовности.
х
о Методы: На основании данных об эксплуатационных отказах и
статистических методов была разработана математическая модель надежности транспортных средств специального < назначения. Используя эту модель и эксплуатационные данные,
путем многокритериальной оптимизации была определена периодичность профилактического обслуживания
транспортных средств с учетом как готовности, так и минимальных затрат на техническое обслуживание. ^ Аналогичный метод был применен для определения оптимальной
^ периодичности профилактического обслуживания 15
компонентов механической системы транспортных средств специального назначения. Групповой анализ был проведен с ш использованием статистического программного обеспечения
МИпКэЬ 15, основанного на теории подобия периодичности профилактического обслуживания 15 компонентов, а статистический анализ проведенного исследования был выполнен с помощью статистического программного обеспечения МИпКэЬ 16.
Результаты: На основании выявленных отказов транспортных средств и их пятнадцати ключевых компонентов были разработаны модели профилактического обслуживания транспортных средств специального назначения. Групповой анализ позволил оптимально сгруппировать пятнадцать компонентов для технического обслуживания, близких по продолжительности эксплуатации между двумя отказами. Благодаря статистическому анализу результатов исследования выявлена функциональную связь оптимальной периодичности профилактического обслуживания
транспортных средств специального назначения.
Выводы: Периодичность технического обслуживания, полученная § в результате многокритериального анализа, является 00
с\1 со
оптимальной, поскольку обеспечивает удовлетворительную готовность автомобиля при оптимальных затратах на техническое обслуживание. Статистический анализ исследования показал, что периодичность технического обслуживания различных компонентов автомобиля различается. В обоих автопарках наиболее длительной периодичностью технического обслуживания оказалось периодичность обслуживания двигателя и трансмиссии. Результаты о
1867
<Л .ф
О ф
>
СР
исследований могут быть использованы для рационализации существующей концепции профилактического обслуживания.
Ключевые слова: транспортное средство, надежность, ш готовность, профилактическое обслуживание, затраты, оптимальная периодичность, многокритериальный анализ.
Одре^ива^е модела превентивног одржава^а возила посебне с
намене ^
го
Стоjко Л. Биочанина, Милица С. Тимот^евиЛ®, £
Жеъко М. Булатови1йв, Милан А. Миши1йг
а Академи]а техничких струковних студи]а Београд, -
Катедра за рачунарско и машинско инженерство, Београд, Република Срби]а, аутор за преписку
б Висока школа струковних студи]а Ваздухопловна академи]а
ш
Београд, Република Срби]а "ё
в Во]нотехнички институт, Београд, Република Срби]а £
г Академи]а струковних студиjа косовско метохи]ска, Звечан, Република Срби]а
го
ОБЛАСТ: машинство .!=
КАТЕГОРИJА (ТИП) ЧЛАНКА: оригинални научни рад
Сажетак:
Увод/циъ: Циъ рада ]есте да се, на основу података из й експлоатаци'е возила, добщу квантитативни и квалитативни показатели стаъа возила и поузданости, на основу щих се може одредити оптимална периодичност превентивног одржаваъа возила посебне намене и прецизни'е управъати токовима процеса >8 одржаваъа возила и ъиховом оперативном готовошПу. ¡§
Методе: На основу података о отказима из експлоатаци'е, доби'ених статистичким методама, одре^ен ¡е математички модел поузданости возила посебне намене. На основу овог модела и података из експлоатаци'е одре^ена ¡е периодичност превентивног одржаваъа возила применом вишекритерцумске оптимизацце, уважава]уПи критерцум готовости и критерцум
о
минималних трошкова одржаваъа. По исто] методологии одре^ена | уе оптимална периодичност превентивног одржаваъа петнаест
(Л склопова моторно-техничког дела возила посебне намене.
Извршена ¡е групна анализа, у статистичком софтверу МтйаЬ 15, на основу теори'е сличности периодичности превентивног
> одржаваъа ових склопова, и статистичка анализа спроведених истраживаъа у статистичком софтверу М1пКаЬ 16. Резултати: Доби'ени су модели превентивног одржаваъа возила
^ посебне намене на основу евидентираних отказа возила и отказа
Е петнаест виталних склопова возила посебне намене. Петнаест
о склопова уе групном анализом груписано у оптималне групе за
^ одржаваъе, ще су сличне по дужини рада измену два отказа.
< Статистичком анализом спроведених истраживаъа одре^ена }е функционална веза оптималних периодичности превентивног
^ одржаваъа возила посебне намене.
ш Закъучак: Периодичност одржаваъа, добцена вишекритерцумском
> анализом, оптимална ¡е ]ер се добща задовоъава}уПа готовост
< возила уз оптималне трошкове одржаваъа. Статистичком анализом истраживаъа дошло се до закъучка да су периодичности одржаваъа склопова возила различите. У обе флоте мотор и трансмиси'а у блоку има}у на}дужу периодичност одржаваъа. Резултати истраживаъа могу се користити у рационализации
< посто]еПег концепта превентивног одржаваъа.
^ К^учне речи: возило, поузданост, готовост, превентивно
одржаваъе, трошкови, вишекритериумска анализа.
>о одржаваъе, трошкови, оптимална периодичност,
0
Paper received on: 16.04.2024.
Manuscript corrections submitted on: 16.11.2024.
Paper accepted for publishing on: 18.11.2024.
© 2024 The Authors. Published by Vojnotehnicki glasnik / Military Technical Courier (www.vtg.mod.gov.rs, BTr.M0.ynp.cp6). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/rs/).
