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Sea state estimation (SSE) is significant to the development of autonomous ships, which can enhance the sustainable development of maritime transportation. Traditional model-based methods are limited by their drawbacks, such as high costs and inaccurate estimations. The deep learning model shows superior performance, but it requires that the sample quantity for each sea state should be almost the same. Since the occurrence probability of each state is different, the ships mainly work in low sea states, and the collected ship motion data for different sea states are highly imbalanced. This work proposes a novel class-imbalanced ship motion data-based cross-scale model for SSE. The model consists of three major components: a multi-scale feature learning module, a cross-scale feature learning module, and a prototype classifier module. The multi-scale and cross-scale feature learning modules are designed to learn abundant coarse and fine-level features from the ship motion data. The prototype classifier is utilized to overcome the limitation of the conventional softmax classifier to produce better estimates. Our research highlights our model’s remarkable scalability and versatility with 30 publicly available datasets in time series classification, demonstrating superior performance over baseline methods in 21 cases. Notably, it outperformed ShapeNet by 5.72% and EDI by 26.3%. We further validated our model’s proficiency using ship motion datasets, consistently surpassing eight state-of-the-art baselines and five class-imbalanced learning methods. Ablation and sensitivity studies, emphasize the critical role of each model component. Our findings underscore the model’s robustness and its potential to advance time series classification in diverse domains.

Leading edge erosion (LEE) repairs of wind turbine blades (WTBs) involve infield application of leading edge protection (LEP) solutions. The industry is currently aiming to use factory based LEP coatings that can applied to the WTBs before they are shipped out for installation. However, one of the main challenges related to these solutions is the choice of a minimum LEP application length to be applied in the spanwise direction of the WTBs. Generally, coating suppliers apply 10–20 m of LEP onto the blades starting from the tip of the blade using the “rule of thumb”, and no studies in the literature exist that stipulate how these LEP lengths can be calculated. In this study, we extend the scope of a recently developed long-term probabilistic framework to determine the minimum LEP application length required for WTBs to combat rain-induced erosion. A parametric study is performed where different wind turbines with varying power ratings of 2.1 MW to 15 MW at different Dutch sites ranging from inland to coastal are considered. The results of the study show that the LEP application length is sensitive to the choice of the site, as well as the turbine attributes. Further, LEP lengths for WTBs are found to be the highest for turbines installed at coastal sites and turbines with higher power ratings. A detailed investigation is further performed to check the sensitivity of the LEP application length with the wind turbine parameters. The results of the study are expected to provide guidelines to the industry for efficient repair strategies for WTBs., Aerospace Manufacturing Technologies

Being one of the most commonly used offshore operations, offshore lifting operations become increasingly challenging due to the gradually growing size and weight of payloads. The research on automatic control in lifting operations, e.g., anti-swing control and heave compensation, only considers simple-shaped payloads, such as lumped-mass rigid points. However, the sizes and orientations of many structures cannot be neglected. To lift heavy and large-scale payloads, larger and higher cranes are required. Alternatively, it is possible to share the total loads by enhancing the number of lift wires that may limit the tension on each lift wire. However, the complicated configuration introduces significant complexity into the design of the automatic anti-swing algorithm, especially to the control allocation module. This paper performs a preliminary study on the anti-swing control of a complex-shaped suspended payload lift using a floating crane vessel and a large number of lift wires. Inspired by the knowledge of inverse dynamics and range-based localization, a general model-free anti-swing control scheme is proposed. The controller has a simple form without considering state–space equations, but it can reduce the pendular payload motion regardless of the detailed system configuration. An offshore wind turbine tower–nacelle–rotor preassembly installation using floating crane vessel is adopted as a case study to verify the performance of the proposed control strategy., Aerospace Manufacturing Technologies

Rain-induced leading-edge erosion (LEE) of wind turbine blades (WTBs) is associated with high repair and maintenance costs. The effects of LEE can be triggered in less than 1 to 2 years for some wind turbine sites, whereas it may take several years for others. In addition, the growth of erosion may also differ for different blades and turbines operating at the same site. Hence, LEE is a site- and turbine-specific problem. In this paper, we propose a probabilistic long-term framework for assessing site-specific lifetime of a WTB coating system. Case studies are presented for 1.5 and 10 MW wind turbines, where geographic bubble charts for the leading-edge lifetime and number of repairs expected over the blade's service life are established for 31 sites in the Netherlands. The proposed framework efficiently captures the effects of spatial and orographic features of the sites and wind turbine specifications on LEE calculations. For instance, the erosion is highest at the coastal sites and for sites located at higher altitudes. In addition, erosion is faster for turbines associated with higher tip speeds, and the effects are critical for such sites where the exceedance probability for rated wind conditions are high. The study will aid in the development of efficient operation and maintenance strategies for wind farms., Aerospace Manufacturing Technologies, Aerospace Structures & Computational Mechanics