This paper aims to present a methodology for intelligent monitoring of marine machinery using performance data. Monitoring of machinery condition is a crucial aspect of maintenance optimisation that is required for the vessel operation to remain sustainable and profitable. The proposed methodology will train models pertinent to specific machinery components using pre-classified performance data and then classify new data points using the models developed. For this, measurements are suitably analysed and processed to retain most of the information (variance) of the original dataset while minimising number of required dimensions. Finally, new data are compared against the models developed to evaluate their condition. The above will provide a flexible but robust framework for the early detection of emerging machinery faults. This will lead to minimisation of ship downtime and increase of the ship’s operability and income through operational enhancement. Case studies that show initial results obtained through main engine data are included.
|Title of host publication||Proceedings of the 2017 Smart Ship Technology Conference|
|Place of Publication||London|
|Publisher||Royal Institution of Naval Architects|
|Number of pages||7|
|Publication status||Published - 24 Jan 2017|
|Event||Smart Ship Technology 2017 - RINA HQ, 8-9 Northumberland Street, London, WC2N 5DA, London, United Kingdom|
Duration: 24 Jan 2017 → 25 Jan 2017
|Conference||Smart Ship Technology 2017|
|Period||24/01/17 → 25/01/17|
- performance measurements
- predictive maintenance
- machine learning
- condition monitoring
Gkerekos, C., Lazakis, I., & Theotokatos, G. (2017). Ship machinery condition monitoring using performance data through supervised learning. In Proceedings of the 2017 Smart Ship Technology Conference (pp. 105-111). London: Royal Institution of Naval Architects.