The maritime industry widely expects to have autonomous and semi-autonomous ships (autoships) in the near future. In order to operate and maintain complex and integrated systems in a safe, efficient and cost-beneficial manner, autoships will require intelligent Prognostics and Health Management (PHM) systems. Deep learning (DL) is a potential area for this development, as it is rapidly finding applications in a variety of domains, including self-driving cars, smartphones, vision systems, and more recently in PHM applications. This paper introduces and reviews four well-established DL techniques recently applied to various practical PHM problems. The purpose is to support creativity and provide inspiration towards PHM based on DL (PHMDL) in autoships and the maritime industry. This paper discusses benefits, challenges, suggestions, existing problems, and future research opportunities with respect to this significant new technology. Index Terms-Autonomous ships, deep learning, maritime industry, prognostics and health management.

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 is the key to ensuring the safe and efficient operation of marine vessels. Currently, reactive maintenance and preventive maintenance are two main approaches used onboard. These approaches are either cost-intensive or labor-intensive. Recently, Prognostics and Health Management has emerged as an optimal way to manage maintenance operations. In such a system, fault prognostics aims to predict the remaining useful life based on the sensor measurements. In this paper, the feasibility of applying data-driven fault prognostics to marine diesel engines is investigated. Real-world run-to-failure data of two independent fault-types in two different engine load profiles are collected from a hybrid power lab. The first profile is used for training and validation, while the second is used for testing. The LSTM networks are used to construct the fault prognostics model. Experiments and comparisons are performed to obtain the optimal structure of the networks. Results show that the proposed method generalizes well on the second profile and provides remaining useful life predictions with high accuracy.

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This paper presents an effective model-based thruster failure detection and isolation method for dynamically positioned (DP) offshore surface vessels. A DP vessel is supposed to maintain its position and heading at a reference point exclusively by means of thrusters. The occurrence of thruster failure may cause significant performance losses. Therefore, it is of great practical importance to timely detect and isolate thruster failures. In our proposed method, according to the prior knowledge of mathematical model of a DP ship, estimated model states can be obtained as reference. Wind disturbances, due to its great influence on the thruster diagnosis of the DP vessel, is taken into account. A new attitude based residual generator is designed. A failure can be identified once it exceeds a threshold. To further isolate the failure, a slide window concept together with a probability analysis is applied to the residual, until a concrete thrust failure is found. Simulation experiments of DP operation under different thruster failure cases are conducted in a professional simulator. The results show the proposed method is able to detect and isolate these thruster failures.

This paper presents a novel green and costeffective ballast water treatment system which will utilize waste heat from propulsion machinery for the destruction of microorganisms in the ballast water in a relatively short time. The high temperature recovered from propulsion machinery combined with the rapid destruction of microorganisms will make the new system more effective than the current similar Ballast Water Management Systems. The research will involve a combination of biological laboratory experiments, numerical simulations, and full scale machinery testing. The project has a potential to lead to the development of methods and knowledge for efficient energy utilization, effective destruction of microorganisms, and reliable methods for sampling and testing of water quality. The latest results obtained demonstrate that the proposed idea works efficiently and effectively, while offering a satisfactory verification with on-site biological experiments.