[
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, 2016.10.1109/TTE.2016.2552720
]Search in Google Scholar
[
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, 2012.10.1016/j.cie.2012.02.002
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
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.2020.03.003.10.1016/j.joes.2020.03.003
]Search in Google Scholar
[
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, 2018.10.1007/s10845-015-1133-6
]Search in Google Scholar
[
7. P. Bzura, “Diagnostic Model of Crankshaft Seals”, Polish Marit. Res., vol. 26, no. 3, 2019, doi: 10.2478/pomr-2019-0044.10.2478/pomr-2019-0044
]Search in Google Scholar
[
8. J. Girtler, “Limiting Distribution of the Three-State Semi-Markov 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-2020-0035.10.2478/pomr-2020-0035
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
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 semi-Markov decision-making processes”, Polish Marit. Res., vol. 28, no. 1, 2021, doi: 10.2478/pomr-2021-0011.10.2478/pomr-2021-0011
]Search in Google Scholar
[
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-2021-0036.10.2478/pomr-2021-0036
]Search in Google Scholar
[
13. M.C. Lin, Z. Tang, M.Q. Ning, and C.Y. Wang, “Condition-Based Maintenance Strategy for Dynamic Performance Detection Based on Wiener Process”, Journal of Ordnance Equipment Engineering, vol. 42, no. 7, pp. 40-45, 2021.
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
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.2021.102984.10.1016/j.jobe.2021.102984
]Search in Google Scholar
[
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, 2021.10.3390/su131810472
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
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, 2011.10.1016/j.mcm.2011.07.042
]Search in Google Scholar
[
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, 2019.10.1108/IJQRM-11-2018-0314
]Search in Google Scholar
[
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, 2011.10.1097/QMH.0b013e318213b07921467901
]Search in Google Scholar
[
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, 2011.10.1007/s40747-021-00279-z
]Search in Google Scholar
[
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.2021.105768.10.1016/j.engfailanal.2021.105768
]Search in Google Scholar
[
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, 2021.10.1088/1757-899X/1190/1/012003
]Search in Google Scholar
[
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, 2015.10.1007/s00170-014-6466-3
]Search in Google Scholar
[
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, 2018.10.1016/j.cie.2017.10.033
]Search in Google Scholar
[
26. G. Kai, D. Matthew, et al., Prognostics: The Science of Making Predictions. California, USA, 2017, pp. 123-135.
]Search in Google Scholar
[
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, 2013.10.1108/IJQRM-Oct-2011-0143
]Search in Google Scholar
[
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, 2021.10.1080/00949655.2021.1929984
]Search in Google Scholar
[
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, 2021.10.1142/S0219477521500437
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
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, 2013.10.1016/j.eswa.2013.07.027
]Search in Google Scholar
[
33. E. Ramadhani, H.R. Pratama, E.G. Wahyuni, “Web-based expert system to determine digital forensics tool using rule-based reasoning approach”, Journal of Physics: Conference Series, vol. 1918, no. 4, 2021.10.1088/1742-6596/1918/4/042003
]Search in Google Scholar
[
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, 2019.10.4018/JDM.2019010101
]Search in Google Scholar
