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Research on preventive maintenance strategies and systems for in-service ship equipment

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
With continuous improvements in the function and performance of ship equipment, mechanisms of failure have become more and more complicated. To avoid over-maintenance or under-maintenance in existing routine ship maintenance strategies, a ship-level method for repair decisions based on the preventive maintenance concept is proposed in this paper. First, the anticipated repair demand levels of key components are calculated using an improved failure mode and effects analysis (FMEA) method; second, a Weibull distribution model is established, and the parameters are estimated using the maximum likelihood estimation (MLE) to predict the characteristic life of the equipment; then, logical decision principles and rule-based reasoning (RBR) are used to determine the ship repair level and repair timing. Finally, the feasibility and application value of the proposed repair strategy were verified by case studies, and a ship-level system for repair decisions was established.
Rocznik
Tom
Strony
85--96
Opis fizyczny
Bibliogr. 34 poz., rys., tab.
Twórcy
autor
  • School of Mechanical Engineering, Hubei University of Technology, Nanli Road, Hongshan District, Wuhan 430068 Wuhan China
autor
  • School of Mechanical Engineering, Hubei University of Technology, Nanli Road, Hongshan District, Wuhan 430068 Wuhan China
autor
  • School of Mechanical Engineering, Hubei University of Technology, Nanli Road, Hongshan District, Wuhan 430068 Wuhan China
Bibliografia
  • 1. E. Skjong, R. Volden, E. Rodskar, et al., “Past, Present, and Future Challenges of the Marine Vessel’s Electrical Power System”, IEEE Trans. Transp. Electrif., vol. 2, pp. 522–537, 2016.
  • 2. R. Ahmad, S. Kamaruddin, “An overview of time-based and condition-based maintenance in industrial application”, Computers & Industrial Engineering, vol. 63, no. 1, pp. 135-149, 2012.
  • 3. X. Li, Y.X. Jia, Y.S. Bai, “Study on Optimal Maintenance Level Based on Preventive Group Maintenance Police”, Fire Control & Command Control, vol. 38, no. 2, pp. 35-39, 2013.
  • 4. X.C. Li, J.B. Hu, Z.H. Zhang, “Simulation analysis of warship deployability with maintenance structures involved”, Chinese Journal of Ship Research, vol. 10, no. 5, pp. 123-128, 2015.
  • 5. V.J. Jimenez, N. Bouhmala, A.H. Gausdal, “Developing a predictive maintenance model for vessel machinery”, Journal of Ocean Engineering and Science, vol. 5, no. 4, pp. 358-386, 2020, doi: 10.1016/j.joes.2020.03.003.
  • 6. I. Emovon, R.A. Norman, A.J. Murphy, “Hybrid MCDM based methodology for selecting the optimum maintenance strategy for ship machinery systems”, Journal of Intelligent Manufacturing, vol. 29, no. 3, pp. 519-531, 2018.
  • 7. P. Bzura, “Diagnostic Model of Crankshaft Seals”, Polish Marit. Res., vol. 26, no. 3, 2019, doi: 10.2478/ pomr-2019-0044.
  • 8. J. Girtler, “Limiting Distribution of the Three-State SemiMarkov Model of Technical State Transitions of Ship Power Plant Machines and its Applicability in Operational Decision-Making”, Polish Marit. Res., vol. 27, no. 2, 2020, doi: 10.2478/pomr-2020-0035.
  • 9. R.X. Wei, C. Lin, T.J. Jiang, “Optimization method of condition-based maintenance decision-making for task-oriented ship fleet”, Journal of Naval University of Engineering, vol. 33, no. 4, pp. 83-89, 2021.
  • 10. P. He, W. Zuo, Y. Wang, “Modeling for Repair Level of Air and Missile Defense Equipment Under Multi-constraints”, Fire Control & Command Control, vol. 45, no. 3, pp. 42-47, 2020.
  • 11. J. Girtler and J. Rudnicki, “The matter of decision-making control over operation processes of marine power plant systems with the use of their models in the form of semiMarkov decision-making processes”, Polish Marit. Res., vol. 28, no. 1, 2021, doi: 10.2478/pomr-2021-0011.
  • 12. R. Zagan, I. Paprocka, M.-G. Manea, and E. Manea, “Estimation of Ship Repair Time Using the Genetic Algorithm”, Polish Marit. Res., vol. 28, no. 3, 2021, doi: 10.2478/pomr-2021-0036.
  • 13. M.C. Lin, Z. Tang, M.Q. Ning, and C.Y. Wang, “ConditionBased Maintenance Strategy for Dynamic Performance Detection Based on Wiener Process”, Journal of Ordnance Equipment Engineering, vol. 42, no. 7, pp. 40-45, 2021.
  • 14. M. Hashemi, M. Asadi, “Optimal preventive maintenance of coherent systems: A generalized Pólya process approach”, IISE Transactions, vol. 53, no. 11, pp. 1266-128, 2021.
  • 15. D.P. Niu, L. Guo, et al., “Preventive maintenance period decision for elevator parts based on multi-objective optimization method”, Journal of Building Engineering, vol. 44, 2021, doi: 10.1016/J.JOBE.2021.102984.
  • 16. A. Sa’ad, A.C. Nyoungue, Z. Hajej, “Improved Preventive Maintenance Scheduling for a Photovoltaic Plant under Environmental Constraints”, Sustainability, vol. 13, no. 18, pp. 10472-10472, 2021.
  • 17. Z. Luo, X. Zhou, Y.D. Shi, “Research on method of warship maintenance structure designing based on requirement of assignment and maintenance”, Journal of Naval University of Engineering, vol. 15, no. 04, pp. 60-64, 2018.
  • 18. Y. Geum, Y. Cho, Y. Park, “A systematic approach for diagnosing service failure: Service-specific FMEA and grey relational analysis approach”, Mathematical & Computer Modelling, vol. 54, no. 11-12, pp. 3126-3142, 2011.
  • 19. P. Mahdad, A.B. Mohammad, G. Kamran, “An integrated approach for healthcare services risk assessment and quality enhancement”, International Journal of Quality & Reliability Management, vol. 37, no. 9/10, pp. 1183-1208, 2019.
  • 20. P.B. Southard, S. Kumar, C.A. Southard, “A modified Delphi methodology to conduct a failure modes effects analysis: a patient-centric effort in a clinical medical laboratory”, Quality Management in Health Care, vol. 20, no. 2, pp. 131-51, 2011.
  • 21. Y. Melih, G. Muhammet, C. Erkan, “A holistic FMEA approach by fuzzy-based Bayesian network and best–worst method”, Complex & Intelligent Systems, vol. 7, pp. 1547- 1564, 2011.
  • 22. S. Boral, S. Chakraborty, “Failure analysis of CNC machines due to human errors: An integrated IT2F-MCDM-based FMEA approach”, Engineering Failure Analysis, vol. 130, 2021, doi: 10.1016/J.ENGFAILANAL.2021.105768.
  • 23. E. Kulcsár, I.G. Gyurika, T. Csiszér, “Increasing the Reliability of FMEA Evaluation by Modifying Rating Scales and Applying Pairwise Comparison Method”, IOP Conference Series: Materials Science and Engineering, vol. 1190, no. 1, 2021.
  • 24. V. Behnam, M. Salimi, M. Charkhchian, “A new FMEA method by integrating fuzzy belief structure and TOPSIS to improve risk evaluation process”, The International Journal of Advanced Manufacturing Technology, vol. 77, no. 1-4, pp. 357-368, 2015.
  • 25. B. Marcia, S. Shankar, et al., “Forecasting fault events for predictive maintenance using data-driven techniques and ARMA modeling”, Computers & Industrial Engineering, vol. 115, pp. 41-53, 2018.
  • 26. G. Kai, D. Matthew, et al., Prognostics: The Science of Making Predictions. California, USA, 2017, pp. 123-135.
  • 27. E.M. Assis, E.P. Borges, “Generalized q-Weibull model and the bathtub curve”, The International Journal of Quality & Reliability Management, vol. 30, no. 7, pp. 720-736, 2013.
  • 28. A.A. Ahmed, A.R. Zahran, M.A. Ismail, “On maximum likelihood estimation of the general projected normal distribution”, Journal of Statistical Computation and Simulation, vol. 91, no. 16, pp. 3453-3472, 2021.
  • 29. P. Nasiri, A.A. Azarian, “Estimation of the Parameters of Generalized Inverse Weibull Geometric Distribution and its Application”, Fluctuation and Noise Letters, vol. 20, no. 05, 2021.
  • 30. B.H. Jia, Y.W. Ma, et al., “Continued Operational Safety Assessment of Civil Aircraft Structural Events Based on TARAM”, Advances in Aeronautical Science and Engineering, vol. 12, no. 5, pp. 1-9, 2021, doi: 10.16615/j. cnki.1674-8190.2021.05.04
  • 31. B. Yang, Y. Chen, M.H. Wang, “Discussion about maintenance mode decision method for warship weapon equipments”, Journal of Gun Launch & Control, vol. 35, no. 01, pp. 83-87, 2014.
  • 32. A. Ngamnij, A. Somjit, “Semantic Ontology Mapping for Interoperability of Learning Resource Systems using a rule-based reasoning approach”, Expert Systems with Applications, vol. 40, no. 18, pp. 7428-7443, 2013.
  • 33. E. Ramadhani, H.R. Pratama, E.G. Wahyuni, “Web-based expert system to determine digital forensics tool using rulebased reasoning approach”, Journal of Physics: Conference Series, vol. 1918, no. 4, 2021.
  • 34. K. Ljubica, K. Zoltan, “Using Ontology and Rule-Based Reasoning for Conceptual Data Models Synonyms Detection: A Case Study”, Journal of Database Management (JDM), vol. 30, no. 1, pp.1-21, 2019.
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-720986b1-6dc5-4131-b73b-6486dde06dea
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