Enhancing resilience in marine propulsion systems by adopting machine learning technology for predicting failures and prioritising maintenance activities

Abstract

With the rapid transition towards Industry 4.0, many industries have recognised the need to develop intelligent maintenance systems and predictive analytics tools that are able to optimise the performance of complex assets throughout their entire lifecycle. This paper adopts a machine learning (ML) technology to enhance the resilience of marine propulsion systems by predicting mean-time between failures (MTBF) and prioritising condition-based maintenance (CBM) activities. The factors considered for the analysis include a series of attributes (in total 16 features) which directly or indirectly represent the state of the system subject to degradation. From the results obtained, it is found out that the feed-forward neural network (FFNN) has the least mean square errors (MSE) and, thus, it is the most suitable method for numerical and discretized estimations. The results from a greedy search algorithm also show that two attributes of ‘gas generator rate of revolutions’ and ‘gas turbine rate of revolutions’ are the most significant parameters influencing system reliability, and therefore, they require closer monitoring. Furthermore, a decision support system (DSS) dashboard is designed to help marine propulsion system operators make informed maintenance decisions and find an improved balance in their spare parts inventory.