Haoran Ye

Cylindrical floating production storage and offloading (FPSO) units are advancing into deeper waters. Overcoming the severe challenges posed by complex deepwater environments requires the design of mooring systems that balance economic efficiency and mooring performance. This paper proposes an innovative optimization method for cylindrical FPSO mooring systems, combining the Kolmogorov–Arnold Network (KAN) with the Non-dominated Sorting Genetic Algorithm III (NSGA-III). Configuration samples are generated within predefined design variable ranges using Latin Hypercube Sampling (LHS), followed by time-domain global response simulations using OrcaFlex (version 11.3) software. A KAN surrogate model is constructed to predict the dynamic responses of the mooring system. Finally, the NSGA-III algorithm is employed for multi-objective optimization to obtain the Pareto optimal set, aiming to minimize mooring costs, maximum tension, fatigue damage, and platform offset. The results demonstrate that, compared to traditional optimization methods, the combination of KAN and NSGA-III exhibits superior prediction accuracy and generalization capabilities. The optimized configurations significantly outperform the original design in both mooring performance and economic costs. Specifically, the most economical scheme reduces mooring costs by 20.73%, the minimum tension scheme decreases mooring line tension by 46.75%, and the minimum displacement scheme reduces platform offset by 30.88%.

The condition monitoring of mooring equipment is an important engineering reliability issue during the operation of a floating production storage and offloading unit (FPSO). The chain jack (CJ) is the key equipment for powering the mooring chain in a spread mooring system. Under complex and dynamic marine operating conditions, different severity faults in the CJ hydraulic system display distinct time-scale characteristics. Hence, this paper proposes a real-time fault diagnosis method of the CJ hydraulic system based on multi-scale feature fusion. Firstly, the model incorporates a convolutional neural network (CNN) layer to extract localized spatial features from multivariate time-series data, effectively identifying fault patterns over the associated short intervals. Subsequently, the bidirectional long short-term memory (BiLSTM) layer is introduced to construct a dynamic temporal model to comprehensively capture the evolution of the fault severity. Finally, a multi-scale global attention mechanism (GAM) emphasizes persistent fault behaviors across time scales, dynamically prioritizing relevant features to improve diagnostic accuracy and model interpretability. The study results indicate that the proposed model’s accuracy improves by 7.36% over the CNN-GAM for 11 failure modes, up to 99.34%. This study contributes to the safe operation of an FPSO by guiding monitoring CJ operations under different load conditions.

Offshore winches are crucial in marine engineering, particularly in marine scientific research and deep-sea exploration. The use of fibre ropes presents significant opportunities for the weight reduction of winches as a consequence of the low length–strength ratio and characteristics of corrosion resistance. Nonetheless, a challenge arises in underestimating the stress load levels in load assessments of multi-layer winch systems using synthetic fibre ropes. Traditional computational methods reliant on symmetrically rotational models fall short in accurately predicting and assessing practical applications. This paper introduces a finite element analysis model based on a non-rotationally symmetric approach with four surfaces subjected to various radial pressure on account of the deformation of the fibre ropes. In the design model, sixteen stress detection paths have been incorporated to identify and confirm non-linear stresses. The outcomes of the finite element simulations have been compared with experimental results with two synthetic fibre ropes, each with distinct deformation characteristics utilised. The findings demonstrate that the application of the model aligns well with experimental results, showcasing its relevance and practical value in real-world scenarios. Precise theoretical calculations and experimental validation are pivotal to ensuring that equipment reliability and safety are maintained alongside the pursuit of light-weighting.