Graph-Structured Data Analysis of Component Failure in Autonomous Cargo Ships Based on Feature Fusion

• URL: http://arxiv.org/abs/2507.13721v1
• Date: Fri, 18 Jul 2025 08:02:49 GMT
• Title: Graph-Structured Data Analysis of Component Failure in Autonomous Cargo Ships Based on Feature Fusion
• Authors: Zizhao Zhang, Tianxiang Zhao, Yu Sun, Liping Sun, Jichuan Kang,
• Abstract summary: This paper proposes a novel hybrid feature fusion framework for constructing a graph-structured dataset of failure modes.<n>A hierarchical feature fusion framework is constructed, using Word2Vec encoding to encode subsystem/component features, BERT-KPCA to process failure modes/reasons, and Sentence-BERT to quantify the semantic association between failure impact and emergency decision-making.
• Score: 20.287188044863925
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To address the challenges posed by cascading reactions caused by component failures in autonomous cargo ships (ACS) and the uncertainties in emergency decision-making, this paper proposes a novel hybrid feature fusion framework for constructing a graph-structured dataset of failure modes. By employing an improved cuckoo search algorithm (HN-CSA), the literature retrieval efficiency is significantly enhanced, achieving improvements of 7.1% and 3.4% compared to the NSGA-II and CSA search algorithms, respectively. A hierarchical feature fusion framework is constructed, using Word2Vec encoding to encode subsystem/component features, BERT-KPCA to process failure modes/reasons, and Sentence-BERT to quantify the semantic association between failure impact and emergency decision-making. The dataset covers 12 systems, 1,262 failure modes, and 6,150 propagation paths. Validation results show that the GATE-GNN model achieves a classification accuracy of 0.735, comparable to existing benchmarks. Additionally, a silhouette coefficient of 0.641 indicates that the features are highly distinguishable. In the label prediction results, the Shore-based Meteorological Service System achieved an F1 score of 0.93, demonstrating high prediction accuracy. This paper not only provides a solid foundation for failure analysis in autonomous cargo ships but also offers reliable support for fault diagnosis, risk assessment, and intelligent decision-making systems. The link to the dataset is https://github.com/wojiufukele/Graph-Structured-about-CSA.

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