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

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.