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 article, 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 that the ferry may encounter. First, the data are subjected to a feature selection process to remove irrelevant and redundant features. Then, a multiregime 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 fault types with different nature of degradation related to the marine diesel engine.

The maritime industry generally anticipates having semi-autonomous ferries in commercial use on the west coast of Norway by the end of this decade. In order to schedule maintenance operations of critical components in a secure and costeffective manner, a reliable prognostics and health management system is essential during autonomous operations. Any remaining useful life prediction obtained from such system should depend on an automatic fault detection algorithm. In this study, an unsupervised reconstruction-based fault detection algorithm is used to predict faults automatically in a simulated autonomous ferry crossing operation. The benefits of the algorithm are confirmed on data sets of real-operational data from a marine diesel engine collected from a hybrid power lab. During the ferry crossing operation, the engine is subjected to drastic changes in operational loads. This increases the difficulty of the algorithm to detect faults with high accuracy. Thus, to support the algorithm, three different feature selection processes on the input data is compared. The results suggest that the algorithm achieves the highest prediction accuracy when the input data is subjected to feature selection based on sensitivity analysis.

Maintenance routines on ships today follow either a reactive maintenance (RM) or preventive maintenance (PvM) approach. RM can be regarded as post-failure repair, which might create large costs. PvM uses predetermined maintenance intervals, which often involves unnecessary maintenance. Recently, prognostics and health management (PHM) has emerged as a potential way to develop an ideal maintenance policy. PHM aims to provide optimal maintenance schedule through the use of sensor measurement for fault detection and fault prognostics, among which fault detection is the first and fundamental action. In this paper, a longshort term memory based variational autoencoder (LSTM-VAE) is proposed for fault detection of maritime components onboard. It is a semi-supervised approach that requires only fault-free data for training. Therefore, it is widely applicable in the maritime industry since operational data in normal conditions already exists. Realworld operation data collected from a diesel engine on the research vessel (RV) Gunnerus is used to validate the method. Results show that the LSTM-VAE can detect the fault accurately.

As the use of fossil fuels becomes more and more restricted there is a need for alternative fuels also at sea. For short sea distance travel purposes, batteries may be a solution. However, for longer distances, when there is no possibility of recharging at sea, batteries do not have sufficient capacity yet. Several projects have demonstrated the use of compressed hydrogen (CH2) as a fuel for road transport. The experience with hydrogen as a maritime fuel is very limited. In this paper, the similarities and differences between liquefied hydrogen (LH2) and liquefied natural gas (LNG) as a maritime fuel will be discussed based on literature data of their properties and our system knowledge. The advantages and disadvantages of the two fuels will be examined with respect to use as a maritime fuel. Our objective is to discuss if and how hydrogen could replace fossil fuels on long distance sea voyages. Due to the low temperature of LH2 and wide flammability range in air these systems have more challenges related to storage and processing onboard than LNG. These factors result in higher investment costs. All this may also imply challenges for the LH2 supply chain.

2021): Coupling of dynamic reaction forces of a heavy load crane and ship motion responses in waves, Ships and Offshore Structures,