In polar environments, the thermoviscous behavior and heat dissipation characteristics of deck hydraulic systems are severely affected, resulting in response delays and increased failure risk during high-load operations such as anchor retrieval. To address the limited availability of polar field test samples and the multi-scale nature of oil-temperature responses—featuring short-term abrupt variations and slow-varying hysteresis—this study proposes a Multi-Scale Attention Transformer (MSA-Transformer). Through parallel multi-scale attention branches, the model collaboratively captures both transient and gradual dynamics, thereby improving prediction robustness under polar extreme cold conditions. Based on anchor-retrieval test data collected in Genhe, China’s Cold Pole, at −30 °C, −35 °C, and −40 °C, a dataset containing 18 load cycles was constructed. Experimental results based on 5-fold stratified cross-validation results show that the MSA-Transformer achieves the best performance across evaluation metrics, attaining an average coefficient of determination (R²) of 0.9119 along with the lowest error rates (MAE, RMSE, MSE) on the test set, thereby outperforming LSTM, CNN-LSTM, and the standard Transformer. This work provides an effective tool for state prediction, maintenance optimization, and anomaly early warning in polar deck hydraulic systems, supporting the intelligent health management of hydraulic equipment.

In response to the problems of high energy consumption and difficulty in precise regulation of electric tracing anti-icing systems for polar marine engineering equipment in low-temperature, strong-wind, and high-humidity environments, this paper conducts experimental measurement and predictive modeling research on the convective heat transfer characteristics of electric heat-traced circular cylinders in cross-flow. First, a controllable environmental experimental system was set up to obtain 144 sets of steady-state convective heat transfer data under different combinations of wind speed, temperature, humidity, and heat flux density. Based on this, a Nusselt number (Nu) prediction model using a fully connected Deep Neural Network (DNN) was constructed, and its performance was evaluated through five-fold cross-validation. The results show that the DNN model can effectively capture nonlinear mapping relationships among multiple factors, and its prediction accuracy ($R^2$ = 0.9828) is superior to that of traditional machine learning models. Furthermore, the Shapley Additive Explanations (SHAP) method was introduced to analyze the multi-factor coupling mechanisms, quantify the contribution of each input variable to the Nu prediction, and provide a data-driven reference for the optimization of engineering parameters under extreme polar conditions.

In polar environments, icing on ship deck surfaces severely compromises navigation safety. Conventional electric trace heating systems operate in continuous heating mode, resulting in high energy consumption. This study proposes an intelligent periodic heating control method that integrates physics-informed neural networks (PINNs) and deep reinforcement learning (DRL) for energy-efficient anti-icing of circular pipe components on polar vessels. Using a polar coupled environment simulation platform, experiments were conducted on electric heating anti-icing for circular pipe components. Temperature data under various heating modes were collected, and a physically constrained PINN temperature prediction model was constructed, achieving high prediction accuracy with limited samples (test set R² = 0.9091; 5-fold cross-validation R² = 0.8877 ± 0.0312). The DRL agent trained in this virtual environment autonomously optimized the heating strategy, yielding optimal cycle parameters: heating ratio D = 0.722 and cycle duration τ = 88 s. While maintaining surface temperatures above 0 °C against a −10 °C ambient baseline, this strategy achieved a unit energy consumption of 0.27 kJ/°C, representing a 63% reduction compared to conventional continuous heating. This study provides a data-physics fusion control approach for polar vessel anti-icing systems, demonstrating strong potential for engineering applications.