Online Fault Detection in Autonomous Ferries: Using fault-type in-dependent spectral anomaly detection

Abstract

Enthusiasm for ship autonomy is flourishing in the maritime industry. In this context, data-driven Prognostics and Health Management (PHM) systems have emerged as the optimal way to improve operational reliability and system safety. However, further research is needed to enhance the essential actions relating to such a system. Fault detection is the first and most crucial action of any data-driven PHM system. In this study, we propose a fault-type independent spectral anomaly detection algorithm for marine diesel engine degradation in autonomous ferries. The benefits of the algorithm are verified on three fault-types where the nature of degradation differs. Both normal operation data and faulty degradation data have been collected from a marine diesel engine, using two different engine load profiles. These profiles aim to replicate real autonomous ferry crossing operations, environmental conditions the ferry may encounter. First, the data is subjected to a feature selection process to remove irrelevant and redundant features. Then, a multi-regime normalization method is performed on the data to merge the engine loads into one context. Finally, a variational autoencoder is trained to estimate velocity and acceleration calculations of the anomaly score. Generic and dynamic threshold limits are simultaneously established to detect the fault time step online. The algorithm achieved an accuracy of 97.66% in the final test when the acceleration was used as the fault detector. The results suggest that the algorithm is independent of faulttypes with different nature of degradation related to the marine diesel engine.