Using artificial neural network-self-organising map for data clustering of marine engine condition monitoring applications

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Raptodimos, Yiannis and Lazakis, Iraklis (2018) Using artificial neural network-self-organising map for data clustering of marine engine condition monitoring applications. Ships and Offshore Structures, 13 (6). pp. 649-656. ISSN 1754-212X (https://doi.org/10.1080/17445302.2018.1443694)

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Abstract

Condition monitoring is the process of monitoring parameters expressing machinery condition, interpreting them for the identification of change which could indicate developing faults. Data processing is important in a ship condition monitoring software tool, as misinterpretation of data can significantly affect the accuracy and performance of the predictions made. Data for key performance parameters for a PANAMAX container ship main engine cylinder are clustered using a two-stage approach. Initially, the data is clustered using the artificial neural network (ANN)-self-organising map (SOM) and then the clusters are interclustered using the Euclidean distance metric into groups. The case study results demonstrate the capability of the SOM to monitor the main engine condition by identifying clusters containing data which are diverse compared to data representing normal engine operating conditions. The results obtained can be further expanded for application in diagnostic purposes, identifying faults, their causes and effects to the ship main engine.

ORCID iDs

Raptodimos, Yiannis ORCID logoORCID: https://orcid.org/0000-0002-7508-5956 and Lazakis, Iraklis ORCID logoORCID: https://orcid.org/0000-0002-6130-9410;
CORE (COnnecting REpositories)