To address the feature drift induced by inter-individual variability in marine main engines and the scarcity of fault data in the target domain, this study leverages digital twin technology and transfer learning to achieve cross-individual fault diagnosis.

Based on marine main engine performance parameters and an adaptive mechanism, a digital twin-enhanced cross-individual fault diagnosis model (DT-DANN) is developed. Particle swarm optimization (PSO) is employed to adaptively tune performance parameters factors, aiming to construct a high-fidelity individual digital twin model and generate healthy-state data samples under variable operating conditions. By integrating a multi-scale one-dimensional convolutional neural network (1D-CNN) with an improved domain adversarial neural network (DANN), a healthy-state anchor strategy is proposed to enable cross-individual fault diagnosis for marine diesel engines.

Simulation results demonstrate that the adaptively tuned performance parameter factors achieve a MAPE of 0.052 0%, while the proposed DT-DANN model reaches a diagnostic accuracy of 100%.

The proposed method effectively mitigates the model mismatch problem caused by inter-individual variability and data scarcity, enabling high-accuracy zero-shot cross-individual fault diagnosis for marine main engines.