Ship machinery condition monitoring using vibration data through supervised learning

Gkerekos, Christos and Lazakis, Iraklis and Theotokatos, Gerasimos; Lazakis, Iraklis and Theotokatos, Gerasimos, eds. (2016) Ship machinery condition monitoring using vibration data through supervised learning. In: Proceedings of MSO 2016, International Conference on Maritime Safety and Operations. University of Strathclyde Publishing, GBR, pp. 103-110. ISBN 9781909522169

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This paper aims to present an integrated methodology for the monitoring of marine machinery using vibration data. Monitoring of machinery 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 using pre-classified (healthy/faulty) data and then classify new data points using the models developed. For this, vibration points are first acquired, appropriately processed and stored in a database. Specific features are then extracted from the data and stored. These data are then used to train supervised models pertinent to specific machinery components. Finally, new data are compared against the models developed in order 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.