Machine learning approaches for improving condition-based maintenance of naval propulsion plants

By Andrea Coraddu, Luca Oneto, A. Ghio, S. Savio, Davide Anguita, M. Figari. 2016

198 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanics of Materials

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In Control and Systems Engineering

1. Eric P. Xing
2. Frank Hutter
3. Aaron Klein
4. Barnab'as P'oczos
5. Jeff Schneider
6. Kirthevasan Kandasamy
7. Holger H. Hoos
8. Kevin Leyton-Brown

Citations in the corpus

• Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization (2019, methodology): “We use 4 popular UCI (Lichman, 2013) datasets for regression: protein structure (Rana, 2013), slice localization (Graf et al., 2011), naval propulsion (Coraddu et al., 2014) and parkinsons telemonitoring (Tsanas et al., 2010).”
• Neural Architecture Search with Bayesian Optimisation and Optimal Transport (2018, methodology): “Datasets: We use the following datasets: blog feedback [4], indoor location [43], slice localisation [11], naval propulsion [5], protein tertiary structure [31], news popularity [7], Cifar10 [21].”
• Big data and artificial intelligence in the maritime industry: a bibliometric review and future research directions (2020, methodology): “Utilising such data, Coraddu et al. (2016) forecast potential future failures of a vessel propulsion system and found that SVM is better than regularised least square (RLS).”