Development of a time series imaging approach for fault classification of marine systems

As part of any Prognostics and Health Management (PHM) system for the shipping industry, the determination of the current health of marine systems is fundamental. As such, diagnostic analytics is performed; a process that is typically constituted by fault detection, fault isolation, and fault identification. Although some efforts have been made to distinguish the faults and malfunctions (fault detection) that can occur in marine systems, the implementation of fault identification to provide a description of any considered fault type and its nature is still an unexplored area due to the lack of fault data. To overcome this, a methodology for the identification of anomalies in marine systems is presented in this paper. The proposed approach aims to analyse the implementation of time series imaging through the application of the first-order Markov chain in tandem with an analysis of both ResNet50V2 and Convolutional Neural Networks (CNNs) as part of the image classification task. To highlight the performance of this methodology, anomalies have been simulated considering the power parameter of a diesel generator. Results demonstrated the potential of time series imaging and image classification approaches, as the Markov-CNN achieved an accuracy of 95% when performing the fault classification task.