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

@inproceedings{Coraddu2016MachineLA,
  title={Machine learning approaches for improving condition-based maintenance of naval propulsion plants},
  author={Andrea Coraddu and Luca Oneto and Aessandro Ghio and Stefano Savio and Davide Anguita and Massimo Figari},
  year={2016}
}
Availability, reliability and economic sustainability of naval propulsion plants are key elements to cope with because maintenance costs represent a large slice of total operational expenses. Depending on the adopted strategy, impact of maintenance on overall expenses can remarkably vary; for example, letting an asset running up until breakdown can lead to unaffordable costs. As a matter of fact, a desideratum is to progress maintenance technology of ship propulsion systems from breakdown or… CONTINUE READING

Topics from this paper.

Citations

Publications citing this paper.
SHOWING 1-10 OF 25 CITATIONS

Proceedings of the 2019 USENIX Conference on Operational Machine Learning

Paul Rausch, Volodymyr Kindratenko, Roy H Campbell
  • 2019
VIEW 14 EXCERPTS
CITES BACKGROUND & METHODS
HIGHLY INFLUENCED

Performance Prediction using Neural Network and Confidence Intervals: a Gas Turbine application

  • 2018 IEEE International Conference on Big Data (Big Data)
  • 2018
VIEW 8 EXCERPTS
CITES BACKGROUND & METHODS
HIGHLY INFLUENCED

CNN-PDM: A Convolutional Neural Network Framework For Assets Predictive Maintenance

Willamos Silva
  • 2019
VIEW 3 EXCERPTS
CITES BACKGROUND & METHODS

Similar Papers

Loading similar papers…