Parallel Channel-Temporal Attention-Based Temporal Convolutional Network for Marine Engine Performance Prediction Under Varying Load Conditions

The safe operation and intelligent optimization of ships heavily rely on the predictive maintenance of marine engine performance. However, existing methods for predicting engine performance face limitations due to the various characteristics of ship engines, which depend on the sailing state and environmental conditions. To address this, a novel deep learning framework, the parallel channel-temporal attention-based temporal convolutional network (PCTA-TCN), is proposed to predict marine engine performance across varying load ranges. The PCTA-TCN framework introduces channel attention and temporal attention mechanisms in two independent branches, adaptively adjusting the focus on important features and temporal dependencies in the measured data. Parallel temporal convolutional network (TCN) backbones are used to extract high-level feature representations from different sequences. Finally, the model generates predictions by performing a nonlinear mapping on the extracted features at multiple levels. Experimental validation using sensor-acquired data demonstrates that PCTA-TCN consistently achieves an ${R}^{{2}}$ value exceeding 0.956 across all datasets. Compared to the baseline TCN model, the proposed method improves the prediction accuracy by 13.95%, 49.81%, 15.64%, and 19.73% under four different load conditions. Furthermore, PCTA-TCN outperforms other state-of-the-art methods, particularly under complex and variable operating conditions.