@article{SUN2026, 
author = {Guangxi SUN and Yunpeng CAO and Lin WANG and Zhengyang ZHANG and Lin ZHU},
title = {Research on cross-individual fault diagnosis of marine main engines based on adaptive mechanisms},
year = {2026},
journal = {Chinese Journal of Ship Research},
volume = {21},
number = {4},
pages = {432-441},
keywords = {fault diagnosis, adaptive adjustment, transfer learning, marine main engine},
url = {https://www.sciopen.com/article/10.19693/j.issn.1673-3185.04917},
doi = {10.19693/j.issn.1673-3185.04917},
abstract = {ObjectiveTo 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. MethodBased 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. ResultsSimulation 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%. ConclusionThe 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.}
}