Tytuł artykułu
Treść / Zawartość
Pełne teksty:
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
In the paper a mathematical model of the process of operating aviation refuelling vehicles supplying fuel to aircraft before flight was developed. The present work is a continuation and supplement to the model contained in [52]. The phase space of the process under study was mapped by a 7-state directed graph of the operation process. To calculate the technical readiness index (𝐾𝑔𝑡 ) Markov chains and processes were used. Also, in Section 3, Results and discussions, optional methods for determining the technical readiness coefficient of a vehicle were provided (𝑘𝑔𝑡 ) , based on the total time of the object in individual operating states. This is an alternative in a situation where the analysed process cannot reach a stable average state indefinitely. Two types of measures were used to determine the readiness, i.e. border probabilities and average times of the object in individual states. In both cases, the basis was statistical databases with operational vehicle data, which enabled the calculation of the readiness index and coefficient.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
art. no. 187888
Opis fizyczny
Bibliogr. 54 poz., rys., tab., wykr.
Twórcy
autor
- Military Academy, Warsaw, Poland
autor
- Faculty of Mechanical Engineering, Military University of Technology, Poland
autor
- Faculty of Mechanical Engineering, Military University of Technology, Poland
autor
- Faculty of Mechanical Engineering, Military University of Technology, Poland
autor
- Faculty of Mechanical Engineering, Military University of Technology, Poland
Bibliografia
- 1. Adjetey-Bahun K, Birregah B, Châtelet E, Planchet J-L. A model to quantify the resilience of mass railway transportation systems. Reliability Engineering & System Safety 2016; 153: 1–14, https://doi.org/10.1016/j.ress.2016.03.015.
- 2. Ahmad F, Tang X-W, Qiu J-N et al. Prediction of slope stability using Tree Augmented Naive-Bayes classifier: Modeling and performance evaluation. Mathematical Biosciences and Engineering 2022; 19(5): 4526–4546, https://doi.org/10.3934/mbe.2022209.
- 3. Ahmad M, Ahmad F, Wróblewski P et al. Prediction of ultimate bearing capacity of shallow foundations on cohesionless soils: A gaussian process regression approach. Applied Sciences (Switzerland) 2021. doi:10.3390/app112110317, https://doi.org/10.3390/app112110317.
- 4. Aulin V, Lyashuk O, Pavlenko O et al. Realization of the logistic approach in the international cargo delivery system. Communications - Scientific Letters of the University of Žilina 2019; 21(2): 3–12. https://doi.org/10.26552/com.C.2019.2.3-12
- 5. Blank C, Park D K, Petruccione F. Quantum-enhanced analysis of discrete stochastic processes. npj Quantum Information 2021. doi:10.1038/s41534-021-00459-2, https://doi.org/10.1038/s41534-021-00459-2.
- 6. Borucka A. Method of testing the readiness of means of transport with the use of semi-markov processes. Transport 2021; 36(1): 75–83, https://doi.org/10.3846/transport.2021.14370.
- 7. Borucka A, Kozłowski E, Parczewski R et al. Supply Sequence Modelling Using Hidden Markov Models. Applied Sciences 2022; 13(1): 231, https://doi.org/10.3390/app13010231.
- 8. Çekyay B, Özekici S. MTTF and availability of semi-Markov missions with non-identical generally distributed component lifetimes. Stochastic Models 2023; 39(2): 414–447, https://doi.org/10.1080/15326349.2022.2112225.
- 9. Cevasco D, Koukoura S, Kolios A J. Reliability, availability, maintainability data review for the identification of trends in offshore wind energy applications. Renewable and Sustainable Energy Reviews 2021. doi:10.1016/j.rser.2020.110414, https://doi.org/10.1016/j.rser.2020.110414.
- 10. Cole R, Egan S J, Schwartz J, Rudinsky S L. The Impact of High-fidelity Simulations on Medical Student Readiness. Military Medicine 2023; 188: 7–14, https://doi.org/10.1093/milmed/usac382.
- 11. Cui L, Chen J, Li X. Balanced reliability systems under Markov processes. IISE Transactions 2019; 51(9): 1025–1035, https://doi.org/10.1080/24725854.2018.1536304.
- 12. Durán O, Aguilar J, Capaldo A, Arata A. Fleet resilience: evaluating maintenance strategies in critical equipment. Applied Sciences (Switzerland) 2021; 11(1): 1–17, https://doi.org/10.3390/app11010038.
- 13. Durczak K, Rybacki P, Sujak A. Application of a Selected Pseudorandom Number Generator for the Reliability of Farm Tractors. Applied Sciences (Switzerland) 2022. doi:10.3390/app122312452, https://doi.org/10.3390/app122312452.
- 14. Englezos P. Technology readiness level of gas hydrate technologies. Canadian Journal of Chemical Engineering 2023; 101(6): 3034–3043, https://doi.org/10.1002/cjce.24673.
- 15. Gołda P, Zawisza T, Izdebski M. Evaluation of efficiency and reliability of airport processes using simulation tools. Eksploatacja i Niezawodnosc 2021; 23(4): 659–669, https://doi.org/10.17531/ein.2021.4.8.
- 16. Gourieroux C, Jasiak J. Time varying Markov process with partially observed aggregate data: An application to coronavirus. Journal of Econometrics 2020. doi:10.1016/j.jeconom.2020.09.007, https://doi.org/10.1016/j.jeconom.2020.09.007.
- 17. Hoang T N, Nguyen T P, Pham M P et al. Assessment of availability, readiness, and challenges for scaling-up hypertension management services at primary healthcare facilities, Central Highland region, Vietnam, 2020. BMC Primary Care 2023. doi:10.1186/s12875-023-02092-8, https://doi.org/10.1186/s12875-023-02092-8.
- 18. Horning S, Leung P, Fitzgerald A, Mrad N. Operational readiness simulator: Optimizing operational availability using a virtual environment. International Journal of Aerospace Engineering 2012. doi:10.1155/2012/425075, https://doi.org/10.1155/2012/425075.
- 19. Huang F, Chen Y, Chen Y, Sun H. Stochastic collocation for optimal control problems with stochastic PDE constraints by meshless techniques. Journal of Mathematical Analysis and Applications 2024. doi:10.1016/j.jmaa.2023.127634, https://doi.org/10.1016/j.jmaa.2023.127634.
- 20. Isaac N, Saha A K. Analysis of refueling behavior of hydrogen fuel vehicles through a stochastic model using Markov Chain Process. Renewable and Sustainable Energy Reviews 2021. doi:10.1016/j.rser.2021.110761, https://doi.org/10.1016/j.rser.2021.110761.
- 21. Jiang Y, Lan G. Convergence and exponential stability of modified truncated Milstein method for stochastic differential equations. Journal of Computational and Applied Mathematics 2024. doi:10.1016/j.cam.2023.115288, https://doi.org/10.1016/j.cam.2023.115288.
- 22. Klimczak T, Paś J, Duer S et al. Selected Issues Associated with the Operational and Power Supply Reliability of Fire Alarm Systems. Energies 2022. doi:10.3390/en15228409, https://doi.org/10.3390/en15228409.
- 23. Koltun K J, Bird M B, Forse J N, Nindl B C. Physiological biomarker monitoring during arduous military training: Maintaining readiness and performance. Journal of Science and Medicine in Sport 2023; 26: S64–S70, https://doi.org/10.1016/j.jsams.2022.12.005.
- 24. Kou L, Qin Y, Jia L, Fu Y. Multistate Reliability Evaluation of Bogie on High Speed Railway Vehicle Based on the Network Flow Theory. International Journal of Software Engineering and Knowledge Engineering 2018; 28(04): 431–451, https://doi.org/10.1142/S0218194018400053.
- 25. Kováč J, Gregor I, Melicherčík J, Kuvik T. Analysis of the Operating Parameters of Wood Transport Vehicles from the Point of View of Operational Reliability. Forests 2023. doi:10.3390/f14071511, https://doi.org/10.3390/f14071511.
- 26. Kozłowski E, Borucka A, Świderski A. Application of the logistic regression for determining transition probability matrix of operating states in the transport systems. Eksploatacja i Niezawodnosc 2020; 22(2): 192–200, https://doi.org/10.17531/ein.2020.2.2.
- 27. Lech B, Paweł S. Resilience Assessment Of Heterogeneous Complex Transport Networks – A General Framework And A Case Study. Proceedings of the 29th European Safety and Reliability Conference (ESREL), Research Publishing Services: 2019: 1381–1388, https://doi.org/10.3850/978-981-11-2724-3_0336-cd.
- 28. Li Y, Vo L, Wang G. Higher order time discretization method for a class of semilinear stochastic partial differential equations with multiplicative noise. Journal of Computational and Applied Mathematics 2024. doi:10.1016/j.cam.2023.115442, https://doi.org/10.1016/j.cam.2023.115442.
- 29. Lopes D, Ferreira J, Rafael S et al. High-resolution multi-scale air pollution system: Evaluation of modelling performance and emission control strategies. Journal of Environmental Sciences (China) 2024; 137: 65–81, https://doi.org/10.1016/j.jes.2023.02.046.
- 30. Ma S, Ma X, Xia L. A unified algorithm framework for mean-variance optimization in discounted Markov decision processes. European Journal of Operational Research 2023; 311(3): 1057–1067, https://doi.org/10.1016/j.ejor.2023.06.022.
- 31. Maan V S, Saini M, Kumar A. Investigation of fuzzy semi-Markovian model for single unit systems with partial failure and Weibull distributed random laws. International Journal of Information Technology (Singapore) 2022; 14(6): 2971–2980, https://doi.org/10.1007/s41870-022-01070-0.
- 32. Maruotti A, Punzo A. Initialization of Hidden Markov and Semi-Markov Models: A Critical Evaluation of Several Strategies. International Statistical Review 2021; 89(3): 447–480, https://doi.org/10.1111/insr.12436.
- 33. Musa I M, Yusuf I. Reliability analysis of a small solar system for a home. International Journal of Quality and Reliability Management 2023; 40(1): 267–279, https://doi.org/10.1108/IJQRM-10-2020-0336.
- 34. Paś J. Issues Related to Power Supply Reliability in Integrated Electronic Security Systems Operated in Buildings and Vast Areas. Energies 2023. doi:10.3390/en16083351, https://doi.org/10.3390/en16083351.
- 35. Restel F J. The Markov reliability and safety model of the railway transportation system. 2015: 303–311, https://doi.org/10.1201/b17399-46.
- 36. Shen H, Zhang Y, Wang J et al. Observer-Based Control for Discrete-Time Hidden Semi-Markov Jump Systems. IEEE Transactions on Automatic Control 2022: 1–7, https://doi.org/10.1109/TAC.2022.3229959.
- 37. Song Y-K, Chung E K, Lee Y S et al. Objective structured clinical examination as a competency assessment tool of students’ readiness for advanced pharmacy practice experiences in South Korea: a pilot study. BMC Medical Education 2023. doi:10.1186/s12909-023-04226-z, https://doi.org/10.1186/s12909-023-04226-z.
- 38. Świderski A, Borucka A, Grzelak M, Gil L. Evaluation of machinery readiness using semi-Markov processes. Applied Sciences (Switzerland) 2020. doi:10.3390/app10041541, https://doi.org/10.3390/app10041541.
- 39. Vindel E, Akinci B, Bergés M. A critical investigation of the readiness of VAV systems to adopt grid-interactive capabilities. Energy and Buildings 2023. doi:10.1016/j.enbuild.2023.112974, https://doi.org/10.1016/j.enbuild.2023.112974.
- 40. Vorapojpisut S, Agrawal K. Modeling of Manufacturing Processes using Hidden Semi-Markov Model and RSSI data. 2022. doi:10.1109/iSAI-NLP56921.2022.9960270, https://doi.org/10.1109/iSAI-NLP56921.2022.9960270.
- 41. Wang J, Miao Y. Optimal preventive maintenance policy of the balanced system under the semi-Markov model. Reliability Engineering and System Safety 2021. doi:10.1016/j.ress.2021.107690, https://doi.org/10.1016/j.ress.2021.107690.
- 42. Wang N, Wu M, Yuen K F. A novel method to assess urban multimodal transportation system resilience considering passenger demand and infrastructure supply. Reliability Engineering & System Safety 2023; 238: 109478, https://doi.org/10.1016/j.ress.2023.109478.
- 43. Wawrzyński W, Zieja M, Tomaszewska J, Michalski M. Reliability assessment of aircraft commutators. Energies 2021. doi:10.3390/en14217404, https://doi.org/10.3390/en14217404.
- 44. Wu L, Shen Y, Che F et al. Evaluating the performance and influencing factors of three portable black carbon monitors for field measurement. Journal of Environmental Sciences (China) 2024; 139: 320–333, https://doi.org/10.1016/j.jes.2023.05.044.
- 45. Xiong X, Sha J, Jin L. Optimizing coordinated vehicle platooning: An analytical approach based on stochastic dynamic programming. Transportation Research Part B: Methodological 2021; 150: 482–502, https://doi.org/10.1016/j.trb.2021.06.009.
- 46. Yeh C-T, Lin Y-K, Yeng L C-L, Huang P-T. Reliability evaluation of a multistate railway transportation network from the perspective of a travel agent. Reliability Engineering & System Safety 2021; 214: 107757, https://doi.org/10.1016/j.ress.2021.107757.
- 47. Yonge J, Schaetzel S, Paull J et al. Optimizing combat readiness for military surgeons without trauma fellowship training: Engaging the “voluntary faculty” model. The journal of trauma and acute care surgery 2023; 95(2S Suppl 1): S31–S35, https://doi.org/10.1097/TA.0000000000004040.
- 48. Zhang Q, Yu S, Han Y, Li Y. Research on the model of a multistate aggregated Markov repairable system. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 2022; 236(2): 266–276, https://doi.org/10.1177/1748006X19887651.
- 49. Zieja M, Ważny M, Jasztal M, Stępień S. Estimation of reliability for aircraft systems as regards the impact of destructive ageing processes. 2020: 2372–2377, https://doi.org/10.3850/978-981-11-2724-3_0630-cd.
- 50. Ziółkowski J, Lęgas A, Szymczyk E et al. Optimization of the Delivery Time within the Distribution Network, Taking into Account Fuel Consumption and the Level of Carbon Dioxide Emissions into the Atmosphere. Energies 2022. doi:10.3390/en15145198, https://doi.org/10.3390/en15145198.
- 51. Ziółkowski J, Małachowski J, Oszczypała M et al. Simulation model for analysis and evaluation of selected measures of the helicopter’s readiness. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 2022. doi:10.1177/09544100211069180, https://doi.org/10.1177/09544100211069180.
- 52. Ziółkowski J, Żurek J, Małachowski J et al. Method for Calculating the Required Number of Transport Vehicles Supplying Aviation Fuel to Aircraft during Combat Tasks. Sustainability (Switzerland) 2022. doi:10.3390/su14031619, https://doi.org/10.3390/su14031619.
- 53. Żurek J, Zieja M, Ziółkowski J. Reliability of supplies in a manufacturing enterprise. 2018: 3143–3148. https://doi.org/10.1201/9781351174664-393
- 54. Żyluk A, Zieja M, Grzesik N et al. Implementation of the Mean Time to Failure Indicator in the Control of the Logistical Support of the Operation Process. Applied Sciences (Switzerland) 2023. doi:10.3390/app13074608, https://doi.org/10.3390/app13074608.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c2ec3685-ede1-4433-aac2-4fbdd3b355e2