@article{ZHOU2026, 
author = {Yi ZHOU and Shuaiqun GAO and Hu GUO and Lieyi DONG and Zhijun SHUAI},
title = {Fault diagnosis of marine screw pumps based on multi-sensor information fusion},
year = {2026},
journal = {Chinese Journal of Ship Research},
volume = {21},
number = {5},
pages = {255-267},
keywords = {marine screw pump, multi-sensor information fusion, convolutional neural network, graph convolutional neural network, vibration measurement, feature extraction},
url = {https://www.sciopen.com/article/10.19693/j.issn.1673-3185.04530},
doi = {10.19693/j.issn.1673-3185.04530},
abstract = {ObjectiveTo address information loss from manual feature extraction and the limited reliability of single-sensor diagnosis, this paper proposes a multi-sensor deep learning method.MethodSensors positioned at four different locations are first modeled as nodes in a graph structure. A parallel four-channel one-dimensional convolutional neural network (1DCNN) then extracts deep features from the raw data of each node. Meanwhile, a dynamic adjacency matrix learning module captures the spatial correlations among the nodes and yields the graph adjacency matrix. Subsequently, a graph convolutional neural network (GCN) fuses the deep features from all four nodes. Finally, the fused features are dimension-reduced and classified to obtain the diagnostic results. ResultsExperimental results show that the proposed multi-sensor fusion model achieves a fault diagnosis accuracy of up to 98.82% on the marine screw pump dataset, significantly outperforming the compared models. ConclusionFusing the deep features extracted by the parallel four-channel 1DCNN with the GCN improves both diagnostic accuracy and fault tolerance.}
}