ZASTOSOWANIE ŁAŃCUCHÓW MARKOWA, WSKAŹNIKA MTBF I UCZENIA MASZYNOWEGO W NIEZAWODNOŚCI TRANSPORTU LOTNICZEGO
##plugins.themes.bootstrap3.article.main##
Abstrakt
Niezawodność transportu lotniczego jest kluczowym aspektem w zwiększaniu zadowolenia pasażerów, łączności sieciowej, bezpieczeństwa, zrównoważenia środowiskowego i wydajności operacyjnej. W branży transportu lotniczego niezawodność krytycznych komponentów i systemów odgrywa ważną rolę w zapewnieniu bezpieczeństwa i wydajności systemów transportu lotniczego. Niniejszy artykuł analizuje integrację zaawansowanych metodologii, w tym łańcuchów Markowa, analizy średniego czasu między awariami (MTBF) i uczenia maszynowego, jako obiecujących sposobów poprawy niezawodności. Ponadto, niniejszy artykuł zawiera przegląd danych eksploatacyjnych, wgląd w przyszłe perspektywy i dyskusje na temat wyzwań, implikacji regulacyjnych i współpracy branżowej, co dodatkowo przyczynia się do kompleksowego zrozumienia zastosowania uczenia maszynowego i analizy MTBF w niezawodności transportu lotniczego. Różnorodne zastosowania i ewoluujące trendy w konserwacji predykcyjnej podkreślają jej znaczenie w kształtowaniu przyszłości praktyk konserwacyjnych w branży transportu lotniczego.
##plugins.generic.usageStats.downloads##
##plugins.themes.bootstrap3.article.details##
Utwór dostępny jest na licencji Creative Commons Uznanie autorstwa – Użycie niekomercyjne – Bez utworów zależnych 4.0 Międzynarodowe.
Bibliografia
Aksoy T., Yüksel S., Dinçer H., Hacıoğlu Ü., Maialeh R., Complex Fuzzy Assessment of Green Flight Activity Investments for Sustainable Aviation Industry. Ieee Access, 2022. https://doi.org/10.1109/access.2022.3226584.
Al-Anziand, Fawaz S., AbuZeina D., A Survey of Markov Chain Models in Linguistics Applications, 2016. https://doi.org/10.5121/csit.2016.61305.
Barrett H., Steven R., Britter R., Waitz I.A., Global Mortality Attributable to Aircraft Cruise Emissions. Environmental Science & Technology, 2010. https://doi.org/10.1021/es101325r.
Baxter G., Srisaeng P., Wild G., An Assessment of Airport Sustainability, Part 1—Waste Management at Copenhagen Airport. Resources, 2018. https://doi.org/10.3390/resources7010021.
Dalkilic S., Improving Aircraft Safety and Reliability by Aircraft Maintenance Technician Training. Engineering Failure Analysis 82 (1 December 2017): 687–94. https://doi.org/10.1016/j.engfailanal.2017.06.008.
Ding S.H., Goh T.T., Tan P.S., Wee S.Ch., Kamaruddin S., Implementation of Decision Tree for Maintenance Policy Decision Making - A Case Study in Semiconductor Industry. Advanced Materials Research 591–593 (2012): 704–7. https://doi.org/10.4028/www.scientific.net/AMR.591-593.704.
D’Amico G., Janssen J., Manca R., Semi-Markov Reliability Models With Recurrence Times and Credit Rating Applications. Journal of Applied Mathematics and Decision Sciences, 2009. https://doi.org/10.1155/2009/625712.
D’Angeli D., Donno A., Generalized Crested Products of Markov Chains. European Journal of Combinatorics, 2011. https://doi.org/10.1016/j.ejc.2010.09.007.
Fan J., Zhao T., Jiao J., Dispatch Reliability of Civil Aviation Simulation Based on Generalized Stochastic Petri Nets (GSPN), 2014. https://doi.org/10.1109/icrms.2014.7107360.
Golda P., Zieja M., Risk Analysis in Air Transport. In Transport Means 2015, Pts I and Ii, 620–23. Kaunas: Kaunas Univ Technology Press, 2015. https://www.webofscience.com/wos/woscc/summary/652200dd-db42-410e-98b1-f75b3334d194-24c3ad68/relevance/1.
Gupta A., Chandra V., Dixit A., Reliability Analysis of a Fault-Tolerant Full-Duplex Optical Wireless Communication Transceiver. Ieee Access, 2023. https://doi.org/10.1109/access.2023.3287335.
Gössling S., Risks, Resilience, and Pathways to Sustainable Aviation: A COVID-19 Perspective. Journal of Air Transport Management, 2020. https://doi.org/10.1016/j.jairtraman.2020.101933.
Huang J., Kwok Po Ng M., Wai Chan P., Wind Shear Prediction from Light Detection and Ranging Data Using Machine Learning Methods. ATMOSPHERE. ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND: MDPI, May 2021. https://doi.org/10.3390/atmos12050644.
Karaoğlu U., Mbah O., Zeeshan Q., Applications of Machine Learning in Aircraft Maintenance. Journal of Engineering Management and Systems Engineering, 2023. https://doi.org/10.56578/jemse020105.
Kim J.-H., Lee K., Unscented Kalman Filter-Aided Long Short-Term Memory Approach for Wind Nowcasting. Aerospace, 2021. https://doi.org/10.3390/aerospace8090236.
Knotts R.M.H., Civil Aircraft Maintenance and Support Fault Diagnosis From a Business Perspective. Journal of Quality in Maintenance Engineering, 1999. https://doi.org/10.1108/13552519910298091.
Lee H., Madar S., Sairam S., Puranik T.G., Payan A.P., Kirby M., Pinon O.J., Mavris D.N., Critical Parameter Identification for Safety Events in Commercial Aviation Using Machine Learning. Aerospace, 2020. https://doi.org/10.3390/aerospace7060073.
Lesko S.A., Aleshkin A., Zhukov D., Reliability Analysis of the Air Transportation Network When Blocking Nodes and/or Connections Based on the Methods of Percolation Theory. Iop Conference Series Materials Science and Engineering, 2020. https://doi.org/10.1088/1757-899x/714/1/012016.
Levin D.A., Peres Y., Wilmer E., Markov Chains and Mixing Times, 2008. https://doi.org/10.1090/mbk/058.
Lewitowicz J., Podstawy eksploatacji statków powietrznych. Vol. 1. Warszawa: Wyd. ITWL, 2001. https://agrochowski.pl/pl/p/Podstawy-eksploatacji-statkow-powietrznych%2C-tom-1/119558.
Lewitowicz J., Uncertainty and Dependability of the Risk Model Applicable to Operation of Aircrafts, Journal of KONBiN No. 1 (13) (2010): 341–48.
Li S., Zhang Z., Cheng X., Reliability Analysis of an Air Traffic Network: From Network Structure to Transport Function. Applied Sciences, 2020. https://doi.org/10.3390/app10093168.
Liu J., Feng Y., Lu Ch., Pan W., Teng D., Vibration Reliability Analysis of Aeroengine Rotor Based on Intelligent Neural Network Modeling Framework. Shock and Vibration, 2021. https://doi.org/10.1155/2021/9910601.
Meyn S., Tweedie R.L., Glynn P.W., Markov Chains and Stochastic Stability, 2009. https://doi.org/10.1017/cbo9780511626630.
Oszczypała M., Konwerski J., Ziółkowski J., Małachowski J., Reliability analysis and redundancy optimization of k-out-of-n systems with random variable k using continuous time Markov chain and Monte Carlo simulation, Reliability Engineering and System Safety, 2024, 242, https://doi.org/10.1016/j.ress.2023.109780.
Post J., Maeckelburg M.Ch., Jagel V., Sammito S., Changes in Vital Signs, Ventilation Mode, and Catecholamine Use During Intensive Care Aeromedical Evacuation Flights. Frontiers in Public Health, 2023. https://doi.org/10.3389/fpubh.2023.1100832.
Reyes‐Garcés N., Gionfriddo E., Gómez‐Ríos G.A., Alam Md.N., Boyacı E., Bojko B., Singh V., Grandy J.J., Pawliszyn J., Advances in Solid Phase Microextraction and Perspective on Future Directions. Analytical Chemistry, 2017. https://doi.org/10.1021/acs.analchem.7b04502.
Sadraey M.H., Aircraft Design: A Systems Engineering Approach. Choice Reviews Online, 2013. https://doi.org/10.5860/choice.51-0912.
Salami D., Sousa C.A., Rosário Martins M., Capinha C., Predicting Dengue Importation Into Europe, Using Machine Learning and Model-Agnostic Methods, 2019. https://doi.org/10.1101/19013383.
Schultz M., Reitmann S., Consideration of Passenger Interactions for the Prediction of Aircraft Boarding Time. Aerospace, 2018. https://doi.org/10.3390/aerospace5040101.
Semi-Markov Processes Applications in System Reliability and Maintenance - Franciszek Grabski w KrainaKsiazek.Pl. Accessed 12 April 2021. https://krainaksiazek.pl/Semi-Markov-Processes-Applications-In-S,9780128005187.html#.
Szkutnik-Rogoż J., Małachowski J., Ziołkowski J., An innovative computational algorithm for modelling technical readiness coefficient: A case study in automotive industry, Computers and Industrial Engineering, 2023, 176, https://doi.org/10.1016/j.cie.2022.108942.
Tawfiq A.A., Osama abed el-Raouf M., Elgawad A.A., Farahat M.A., Reliability Assessment for Electrical Power Generation System Based on Advanced Markov Process Combined With Blocks Diagram. International Journal of Electrical and Computer Engineering (Ijece), 2021. https://doi.org/10.11591/ijece.v11i5.pp3647-3659.
Tien H., Sawadsky B., Lewell M., Peddle M., Durham W., Critical Care Transport in the Time of COVID-19. Canadian Journal of Emergency Medicine, 2020. https://doi.org/10.1017/cem.2020.400.
Vidyasagar M., An Elementary Derivation of the Large Deviation Rate Function for Finite State Markov Chains. Asian Journal of Control, 2013. https://doi.org/10.1002/asjc.806.
Wang H., Chen M., Yao X., Liu Z., Wang X., Tate D., Fatigue Reliability Analysis and Design for Structural Components in Quasi‐One‐Shot Device. Pamm, 2018. https://doi.org/10.1002/pamm.201800410.
Wang Y., Cheng H., A Study on Bayesian Method for Reliability Evaluation of Small Sample Size Aviation Support Systems. Journal of Physics Conference Series, 2022. https://doi.org/10.1088/1742-6596/2369/1/012079.
Wang Y., Shao P., Wu Q., Chen M., Reliability Analysis for a Hypersonic Aircraft’s Wing Spar. Aircraft Engineering and Aerospace Technology 91, no. 4 (2019): 549–57. https://doi.org/10.1108/AEAT-11-2017-0242.
Woch M., Zieja M., Janicki M., Tomaszewska J., Statistical Analysis of Aviation Accidents and Incidents Caused by Failure of Hydraulic Systems. Matec Web of Conferences, 2019. https://doi.org/10.1051/matecconf/201929101005.
Zachariah R.A., Sharma S., Kumar V., Systematic Review of Passenger Demand Forecasting in Aviation Industry. Multimedia Tools and Applications, 2023. https://doi.org/10.1007/s11042-023-15552-1.
Żurek J., Review of the safety evaluation methods in aviation, Problemy Eksploatacji nr 4 (2009): 61–70.
Żurek J., Tomaszek H., Zieja M., Analysis of Structural Component’s Lifetime Distribution Considered from the Aspect of the Wearing with the Characteristic Function Applied, 1 January 2014, 2597–2602. https://doi.org/10.1201/b15938-391.
Żyluk A., Zieja M., Grzesik N., Tomaszewska J., Kozłowski G., Jasztal M., Implementation of the Mean Time to Failure Indicator in the Control of the Logistical Support of the Operation Process. Applied Sciences 13, no. 7 (April 2023): 4608. https://doi.org/10.3390/app13074608.