Abstract
Prescriptive analytics is a novel maintenance approach by recommending the maintainer, the right maintenance action at the right time through prioritization of maintenance based on the dynamic risk assessment. It assists in improving the existing preventive maintenance approach by offering a dynamic maintenance plan based on risk or risk-based maintenance scheduling. To date, there is no clear definition of risk-based maintenance as well as its framework, and its application has not been applied extensively in naval domain, which is peculiar due to its function-system relationship compared to the plant maintenance. This paper applies risk-based maintenance decision-making framework in naval domain using prescriptive analytics for optimal maintenance scheduling to improve asset availability and reduce maintenance costs while meeting stakeholders’ expectations. Objectives and considerations in the case study are also defined for peculiarities application of naval vessels. The novel dynamic risk-based maintenance methodology also aims to improve the effectiveness of the current preventive maintenance practice and the inconsistency issue of the existing maintenance decision-making model using the expert judgment in reliability-centered maintenance.
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References
Cipollini F, Oneto L, Coraddu A, Murphy AJ, Anguita D (2018). Condition-Based maintenance of naval propulsion systems with supervised data analysis. https://doi.org/10.1016/j.oceaneng.2017.12.002
Cullum J, Binns J, Lonsdale M, Abbassi R, Garaniya V (2018) Risk-Based Maintenance Scheduling with application to naval vessels and ships. Ocean Eng 148:476–485. https://doi.org/10.1016/j.oceaneng.2017.11.044
Delhi N, Gugulothu N, Tv V, Malhotra P, Vig L, Agarwal P, Shro G (2017) Predicting remaining useful life using time series embeddings based on recurrent neural networks ∗. https://doi.org/10.1145/nnnnnnn.nnnnnnn
Kimera D, Nangolo FN (2020) Maintenance practices and parameters for marine mechanical systems: a review. J Qual Maint Eng 26(3):459–488. https://doi.org/10.1108/JQME-03-2019-0026
Lazakis I, Raptodimos Y, Varelas T (2017). Predicting ship machinery system condition through analytical reliability tools and artificial neural networks. https://doi.org/10.1016/j.oceaneng.2017.11.017
Muhammad MB, Sarwar U, Tahan M, Karim ZAA (2017) Intelligent fault diagnostic model for rotating machinery. 1858–1862
Sena Eruguz A, Tan T, van Houtum G-J (2017) A survey of maintenance and service logistics management: Classification and research agenda from a maritime sector perspective. Comput Oper Res 85:184–205. https://doi.org/10.1016/j.cor.2017.03.003
Simion D, Purcărea A, Cotorcea A, Nicolae F (2020) Maintenance onboard ships using computer maintenance management system. Sci Bull Nav Acad, 23(1), 134–141. https://doi.org/10.21279/1454-864X-20-I1-017
Tahan-bouria M, Muhammad M, Karim ZAA (2016). Adaptive Neuro-Fuzzy inference system for performance health monitoring of industrial gas turbines. 1365–1373
Tahan M, Tsoutsanis E, Muhammad M, Karim ZAA (2017) Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines : A review. Appl Energy 198:122–144. https://doi.org/10.1016/j.apenergy.2017.04.048
Tang Y, Liu Q, **g J, Yang Y, Zou Z (2017) A framework for identification of maintenance significant items in reliability centered maintenance. Energy 118:1295–1303. https://doi.org/10.1016/j.energy.2016.11.011
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© 2024 Institute of Technology PETRONAS Sdn Bhd (Universiti Teknologi PETRONAS)
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Mohd Adha, M., Masdi, M., Hilmi, H. (2024). Prescriptive Analytics for Dynamic Risk-Based Naval Vessel Maintenance Decision-Making. In: Ahmad, F., Iskandar, T., Habib, K. (eds) ICREEM 2022. ICREEM 2022. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-99-5946-4_28
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DOI: https://doi.org/10.1007/978-981-99-5946-4_28
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