Exploiting machine learning for ship systems anomaly detection and healthiness forecasting
Gkerekos, Christos and Lazakis, Iraklis and Theotokatos, Gerasimos; (2018) Exploiting machine learning for ship systems anomaly detection and healthiness forecasting. In: Proceedings of the 2018 Smart Ship Technology Conference. Royal Institution of Naval Architects, GBR.
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This paper describes a novel methodology concerning the application of machine learning for the intelligent monitoring of ship systems. Monitoring of machinery condition is a crucial aspect of maintenance optimisation that is required for the vessel operation to remain sustainable and profitable. Operator-approved performance data are used to train a data-driven Expected Behaviour Model (EBM). Once trained, this model accepts newly-acquired data points as input and returns the probability of them belonging to the same performance profile with training data. Through this, emerging anomalies can be detected. This tool is coupled with a short-term healthiness forecasting tool, able to estimate future healthiness index. This combination allows the derivation of a healthiness score of both current and future condition of ship machinery. Additionally, in cases of performance degradation, the Remaining Useful Life (RUL) of given system can be projected. This provides a robust framework for the early detection of incipient machinery faults.
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Item type: Book Section ID code: 63608 Dates: DateEvent23 January 2018Published10 January 2018AcceptedSubjects: Naval Science > Naval architecture. Shipbuilding. Marine engineering Department: Faculty of Engineering > Naval Architecture, Ocean & Marine Engineering Depositing user: Pure Administrator Date deposited: 04 Apr 2018 15:54 Last modified: 08 Apr 2024 13:46 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/63608