Fouling organisms reduce a ship’s fuel efficiency and disturb the ecosystem. Therefore, the International Maritime Organization (IMO) and many nations have enacted laws that mandate periodic hull cleaning for removing fouling organisms. However, cleaning niche areas of the ship hull is extremely difficult. Due to their complex shape, applying antifouling paint and cleaning with hull cleaning robots is difficult, but about 80% of fouling organisms are concentrated in the niche areas. To resolve this issue, this research proposes the use of an autonomous cleaning robot with a hydraulic polyarticular robot arm to clean niche areas of the ship hull. This robot can approach niche areas of the ship hull with complex shapes using its polyarticular arm. It was designed to be able to scan the cleaning area, establish a cleaning plan, and clean accordingly. This robot autonomously cleaned a propeller blade, which is a typical niche area of the ship hull, to verify the applicability of this system. The experiment results show that approximately 80% of the biofouling was removed from the hull crevices and 81% of the cleaned biofouling was recovered.

There is a large risk of damage, triggered by harsh ocean environments, associated with offshore structures, so structural health monitoring plays an important role in preventing the occurrence of critical and global structural failure from such damage. However, obstacles, such as applicability in the field and increasing calculation costs with increasing structural complexity, remain for real-time structure monitoring offshore. Therefore, this study proposes the comparison of cosine similarity with sensor data to overcome such challenges. As the comparison target, this method uses the rate of changes of natural frequencies before and after the occurrence of various damage scenarios, including not only single but multiple damages, which are organized by the experiment technique design. The comparison method alerts to the occurrence of damage using a normalized warning index, which enables workers to manage the risk of damage. By comparison, moreover, the case most similar with the current status is directly figured out without any additional analysis between monitoring and damage identification, which renders the damage identification process simpler. Plus, the averaged rate of errors in detection is suggested to evaluate the damage level more precisely, if needed. Therefore, this method contributes to the application of real-time structural health monitoring for offshore structures by providing an approach to improve the usability of the proposed technique.

In rotating motors, an electromagnetic analysis using finite-element analysis is performed according to the time interval such as the steps of the rotation angle. If the uncertainty of time variable is necessary to be handled, the time interval should become smaller than uncertainty values. It causes an increase of the cost even if the surrogate model is applied. Thus, space-time kriging surrogate model is proposed to consider time variable as well as space variable in the design stage. The proposed model handles time variable and predicts responses between time intervals to obtain outputs for the tiny uncertainty of time variable. The performance of prediction is verified with simulation data and then the reliability estimated by the proposed model is compared with measured data from manufactured prototypes.

Regular inspections and hull cleanings are essential to prevent bio-fouling on ships. However, traditional cleaning methods such as brush cleaning and high-pressure water-jet cleaning at docks are ineffective in cleaning niche areas like bow thrusters and sea chests. Consequently, cleaning robots based on brushes and water jets have been developed to effectively remove bio-fouling. However, there are concerns that brushes may damage hull coatings, allowing bio-fouling to penetrate the damaged areas. In this study, removal experiments were conducted to identify the most dominant factor in fouling removal using water jet-based cleaning, in preparation for the development of non-contact cavitation high-pressure water jet-cleaning robots. The Taguchi method was used to identify influential factors and generate experimental conditions, and equipment systems for the removal experiments were established. Image analysis was performed to assess the bio-fouling occurrences on each specimen before and after cleaning, and numerical simulations of the nozzle were conducted to estimate stagnation pressure and wall shear stress to confirm the effect on micro-fouling removal. The results indicated that pump pressure is the most influential factor in removing large bio-fouling organisms grown in marine environments and on ship surfaces.