Mechanism–Data Fusion Modeling and Cross-Condition Fault Diagnosis of Typical Faults in Marine Solid Oxide Fuel Cell Power Systems

Solid oxide fuel cell (SOFC) systems in shipboard power plants exhibit strong thermal–electrochemical coupling and are highly sensitive to both balance-of-plant and stack-related faults under changing operating conditions. In this study, a mechanism–data fusion dynamic model of a standalone SOFC system is developed in MATLAB/Simulink by integrating electrochemical equations with mass, species, and energy conservation and key balance-of-plant components. The model is validated against experimental data, with errors of 0.4–2.8%. Based on the validated model, fuel leakage and electrode delamination are introduced to investigate compound and sequential cross-condition faults. The present results show that fuel leakage causes the most severe degradation in current, power, and temperature, whereas electrode delamination mainly reduces current and power by decreasing the effective reaction area. Compound and sequential faults exhibit non-superimposable dynamic evolution, indicating significant fault interaction effects. A partially monotone decision tree combined with point-biserial correlation is then applied for fault diagnosis. The overall diagnostic accuracy for compound faults reaches 88.5%, while the proposed segmented cross-condition strategy improves the peak accuracy for sequential faults to 87.5%. These results provide an effective framework for SOFC fault modeling and diagnosis under variable operating conditions.