To address the degradation and failure of pressure sensors in the sealing water system of dredger mud pumps, an interpretable fault diagnosis and health assessment framework that integrates hydraulic physical principles of sealing pumps with data-driven modeling is established. Firstly, a physical model of sealing water pressure is constructed based on the hydraulic characteristics of the sealing water pump to derive the sealing water pressure at the pump outlet, which serves as a physically groundedreference reference independent of the sensor health state. Considering the nonlinear degradation and parameter drift of the equipment during long-term operation, dynamically optimized power-law fitting coefficients are further introduced to improve the adaptability and accuracy of the physical model under different operating conditions. A multi-scale convolution-Transformer attention fusion network incorporating physical information constraints is developed, where a piecewise dynamic loss-weight scheduling strategy is introduced to achieve synergistic optimization between physical consistency and data-driven fitting accuracy, thereby significantly enhancing sealing-water-pressure prediction accuracy and improving generalization capability under complex operating conditions. Based on the residual variations among the theoretical pressure, the model-predicted pressure, and the sensor-measured pressure, a threshold-based discrimination mechanism is established to realize explainable identification of transient, intermittent, and permanent faults. Meanwhile, a multi-parameter fusion reliability calculation method is proposed to quantify the full-life degradation trajectory of the pressure sensor from a healthy state to initial abnormality, accelerated deterioration, and functional failure, from healthy state, initial abnormality, accelerated deterioration to functional failure and the reliability curve can accurately present the degradation evolution characteristic of "slow-accelerated-sharp decline″. Simulation results based on real vessel operation data show that the proposed method outperforms the comparison models in prediction accuracy, stability, and convergence, with R2 up to 0.952 7, and can identify short-term anomalies in the healthy stage, realizing high-confidence fault diagnosis and intelligent maintenance support for pressure sensors.