Bayesian and machine learning-based fault detection and diagnostics for marine applications

Marine maintenance can improve ship performance by leveraging predictive maintenance, Machine Learning and Data Analytics. This paper aims to enrich the literature, by developing a novel framework for ship diagnostics based on operational data and the probability of faults. Moreover, the framework can identify the root cause of developing faults avoiding black-box Neural Networks, and complex physics-based models. This research integrates Machine Learning-based Fault Detection, Exponentially Weighted Moving Average control charts, and Bayesian diagnostic networks which allow the examination of the rate of development (fault profile) of faults and failure modes. For validation, the case study of a marine Main Engine is used to examine faults in the engine's Air Cooler and Air and Gas Handling System. It is concluded that any simultaneous abnormal deviations in the Main Engine's Exhaust Gas Temperature are more likely to be caused by a fault in the Air and Gas Handling System.