Prescriptive Analytics for Dynamic Risk-Based Naval Vessel Maintenance Decision-Making
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-5946-4_28
Reference11 articles.
1. 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
2. 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
3. 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
4. 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
5. 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
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