FTRG-Net: A Multi-Step Forecasting Method for Exhaust Gas Temperature of Marine Diesel Engines Based on Frequency-Aware Trend-Residual Learning

The exhaust gas temperature (EGT) of marine diesel engines is an important parameter reflecting the engine’s operating conditions. Its variation is influenced by complex thermodynamic processes, including combustion fluctuations and thermal inertia effects, and exhibits significant non-stationarity and multiscale fluctuation characteristics, posing considerable challenges for multi-step forecasting. To address this issue, this paper proposes a multi-step EGT forecasting method for marine diesel engines based on frequency-aware trend-residual learning. This method first extracts long-term variation information from the EGT sequence, then employs frequency-domain analysis to enhance and characterize short-term fluctuation components, and applies temporal feature learning with an adaptive fusion strategy to effectively integrate information across different time scales, thereby improving the accuracy of multi-step EGT forecasting. Based on actual ship operation data, multi-step EGT forecasting tasks with different horizons are established, and the proposed method is compared with several typical deep learning models. Experimental results show that the proposed method achieves competitive and consistent performance across all forecasting horizons. For one-step to four-step forecasting tasks, the mean absolute errors are 0.3078, 0.5037, 0.6835 and 0.8312, respectively, all of which are lower than those of the comparison models. Moreover, the proposed method demonstrates stable performance in evaluation metrics such as mean squared error. These results verify the effectiveness of the proposed method.