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Gas turbine reliability estimation to reduce the risk of failure occurrence with a comparative study between the two-parameter Weibull distribution and a new modified Weibull distribution

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Responding to the needs of quality and robustness of analysis and management of degradation of equipment, to increase their life cycle and to expand these facilities to become more and more sophisticated and agronomic. This work proposes a contribution to increase the survival of a gas turbine, installed in a gascompression plant, with a comparative study between the two-parameter Weibull distribution. A new modified Weibull distribution was proposed also to reduce the risk of occurrence of failure in this rotating machine. A Statistical analysis and validation on the synthesis of turbine's reliability data and failures were considered, with a particular focus on the use of this data to increase the availability of this type of machine. So, developing a maintenance plan based on their reliability indices for scheduled inspections.
Czasopismo
Rocznik
Strony
no. art. 2022107
Opis fizyczny
Bibliogr. 48 poz., rys., tab.
Twórcy
  • Applied Automation and Industrial Diagnostics Laboratory, Faculty of Science and Technology, University of Djelfa 17000 DZ, Algeria
  • Gas Turbine Joint Research Team, University of Djelfa, Djelfa 17000 DZ, Algeria
  • Applied Automation and Industrial Diagnostics Laboratory, Faculty of Science and Technology, University of Djelfa 17000 DZ, Algeria
  • Gas Turbine Joint Research Team, University of Djelfa, Djelfa 17000 DZ, Algeria
  • Faculty of Science and Technology, University of Bordj Bou Arreridj, 34030 DZ, Algeria
  • Applied Automation and Industrial Diagnostics Laboratory, Faculty of Science and Technology, University of Djelfa 17000 DZ, Algeria
  • Gas Turbine Joint Research Team, University of Djelfa, Djelfa 17000 DZ, Algeria
Bibliografia
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  • 3. Djeddi AZ, Hafaifa A, Kouzou A, Abudura S. Exploration of reliability algorithms using modified Weibull distribution: Application on gas turbine. International Journal of System Assurance Engineering and Management, 2017;8:1885-1894. https://doi.org/10.1007/s13198-016-0480-9.
  • 4. Djeddi AZ, Hafaifa A, Salam A. Gas turbine reliability model based on tangent hyperbolic reliability function. Journal of Theoretical and Applied Mechanics, 2015; 53(3):723-730. https://doi.org/10.15632/jtampl.53.3.723.
  • 5. Jeddi AZ, Hafaifa A, Guemana M, Kouzou A. Gas turbine reliability modelling based on a bath shaped rate failure function: modified Weibull distribution validation. Life Cycle Reliability and Safety Engineering, 2020;9:437-448. https://doi.org/10.1007/s41872-020-00149-6.
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  • 42. Xiao YQ, Liu ZY, Zhu W, Peng XM. Reliability assessment and lifetime prediction of TBCs on gas turbine blades considering thermal mismatch and interfacial oxidation. Surface and Coatings Technology, 2021;423:127572. https://doi.org/10.1016/j.surfcoat.2021.127572.
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  • 44. Yang X, Bai M, Liu J, Liu J, Yu D. Gas path fault diagnosis for gas turbine group based on deep transfer learning. Measurement, 2021;181:109631. https://doi.org/10.1016/j.measurement.2021.109631.
  • 45. Shen Y, Khorasani K. Hybrid multi-mode machine learning-based fault diagnosis strategies with application to aircraft gas turbine engines. Neural Networks, 2020;130:126-142. https://doi.org/10.1016/j.neunet.2020.07.001.
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Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9de2d544-4645-4abd-b5c9-ba5e0b716777
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