A time–frequency transformer-CNN gated hybrid framework for ship pump remaining useful life prediction

Shipborne pump systems play a crucial role in transportation, drainage, and auxiliary operations, directly supporting the safe operation of ships and the daily activities of crew members. Accurate prediction of the remaining useful life (RUL) of such systems is therefore essential for early fault awareness, maintenance planning, and mission assurance. Although RUL prediction has been widely investigated for components such as rolling bearings and aircraft engines, effective prediction approaches specifically developed for marine mechanical pump systems are still lacking. To fill this research gap, this study proposes a time–frequency Transformer–convolutional neural network (CNN) gated (TFTCG) model for pump RUL prediction. Vibration signals are characterized by complex temporal structures, exhibiting both short-term transient behaviors and long-term degradation trends. To capture these properties, CNNs are employed to extract local and detailed features, while a transformer-based architecture is introduced to model global dependencies and degradation patterns throughout the entire lifecycle. In addition, a gating mechanism is incorporated to adaptively fuse time-domain and frequency-domain representations, enabling effective integration of local anomalies and global degradation trends and further improving prediction performance. The proposed model jointly addresses fault diagnosis and RUL prediction, allowing it to simultaneously determine the current degradation stage of the pump and predict its future degradation trajectory from real-time input data. Comparative experiments conducted on the C-MAPSS dataset demonstrate the superior performance of the TFTCG model, which achieves a lower root mean square error of 9.76 and a Prognostics and Health Management (PHM) score of 124 compared with existing approaches. The effectiveness and generalization capability of the proposed method are further validated using a simulated marine mechanical pump dataset with an RMSE of 4.87 and a PHM score of 11.21.