Investigating an SVM-driven, one-class approach to estimating ship systems condition

ABSTRACT

Maintenance is a major point that can affect vessel operation sustainability and profitability. Recent literature has shown that condition monitoring of ship systems shows great potential, albeit at significant data requirement costs. In this respect, this paper presents a novel methodology for intelligent, system-level engine performance monitoring, utilising noon-report data with minimal data assumptions. The proposed methodology is based on the training of a one-class Support Vector Machine, which models a diesel generator’s normal behaviour. Unseen data are then input into the model, where its output reflects a gauge of their normality, compared to the training dataset. This aids the dynamic detection of ship machinery incipient faults, contributing to the minimisation of ship downtime. A case study presenting applications of this modelling approach on ship machinery raw data is included, complemented by a sensitivity analysis. This demonstrates the applicability of the developed methodology in identifying deviant, abnormal ship machinery conditions.

KEYWORDS:

• Ship machinery
• condition monitoring
• SVM
• machine learning
• diesel-generator set
• behavioural prediction

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Iraklis Lazakis http://orcid.org/0000-0002-6130-9410

Christos Gkerekos http://orcid.org/0000-0002-3278-9806

Gerasimos Theotokatos http://orcid.org/0000-0003-3547-8867

Additional information

Funding

The work presented in this paper is partially funded by INCASS project. INCASS Project has received research funding from the European Union's Seventh Framework Programme [EU FP7] [grant agreement number 605200]. This publication reflects only the authors' views and the European Union is not liable for any use that may be made of the information contained within.