A Data-Driven Fault Diagnosis Method for Marine Steam Turbine Condensate System Based on Deep Transfer Learning

Accurate fault diagnosis in marine steam turbine condensate systems is challenged by insufficient real fault samples and dynamic operational conditions. To address this limitation, DTL-DFD, a novel framework integrating digital twins (DTs) and deep transfer learning (DTL), is proposed, wherein a high-fidelity physics-constrained digital twin model is constructed through the systematic injection of six diagnostic classes (1 normal + 5 faults), including insufficient circulation water flow.Through an innovative all-layer parameter initialization with a partial fine-tuning (ALPT-PF) strategy, all weights and biases from a pre-trained one-dimensional convolutional neural network (1D-CNN) were fully transferred to the target model, which was subsequently fine-tuned via a hierarchical learning rate mechanism to adapt to real-world distribution discrepancies. Experimental results demonstrate 94.34% accuracy on cross-distribution test sets with a 4.72% improvement over state-of-the-art methods, confirming significant enhancements in generalization capability and diagnostic stability under small-sample conditions with significant real data reduction, thereby providing an effective solution for the intelligent operation and maintenance of marine steam turbine systems.