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 long-short 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. Real-world 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.

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.

Understanding current environmental conditions is essential for autonomous ships, among which real-time estimation of sea conditions is a key aspect. Considering the ship as a large wave buoy, the sea state can be estimated from motion responses without extra sensors installed. This task is challenging since the relationship between the wave and the ship motion is hard to model. Existing methods include a wave buoy analogy (WBA) method, which assumes linearity between wave and ship motion, and a machine learning (ML) approach. Since the data collected from a vessel in the real world are typically limited to a small range of sea states, the ML method might fail when the encountered sea state is not in the training dataset. This article proposes a hybrid approach that combines the above two methods. The ML method is compensated by the WBA method based on the uncertainty of estimation results, and thus, the failure can be avoided. Real-world historical data from the Research Vessel Gunnerus are applied to validate the approach. Results indicate that the hybrid approach improves the estimation accuracy.

Digitalization has become a key aspect of making maritime industries more innovative, efficient, and fit for future operations. One of the most attractive aspects is the concept of digital twins , which refers to digital replicas of physical assets, processes, and systems that can be used as advanced tools for design, operation, and maintenance. This article introduces the development of the digital twin of the research vessel (RV) Gunnerus in Norway, which will be a significant scientific and operational achievement for the maritime industry, making efficient and safe offshore operations possible. It enables data exchange safely and easily among different subsystems, modules, and various applications. Thus, the twin ship can provide an integrated view of the ship’s various physical and behavioral aspects in different stages and allow simultaneous optimization of functional performance requirements. In addition, it enables advanced control and optimization, e.g., creating more reliable prediction for flexible objectives (time, output, emissions, fuel consumption), and executing day-ahead and long-term planning for operations. Several related applications are presented in the end to confirm the effectiveness of the digital twin ship system.

Navigation safety is of paramount importance in areas with heavy and complex maritime traffic. Any ship navigating such a scenario should be able to foresee the future positions of other ships and adjust its path accordingly to avoid collisions. However, predicting future trajectories is a very challenging problem due to many possible future trajectories from the inherent uncertainty and the complex interaction dynamics between different ships. In this article, we propose a deep generative model based on the conditional variational autoencoder framework to learn marine vessel movement and predict future trajectories. The model is able to produce a multimodal probability distribution over future trajectories and model the complex interactions between vessels. Experiments are performed in two-vessel encounter scenarios from real-world automatic identification system data. The proposed model outperforms the baseline methods, including both kinematics-based and data-driven methods. The trajectories predicted by the proposed model are also analyzed to demonstrate the effectiveness of the model.