RESEARCH PAPER
Analysis of Fault Events in Rail Transit Vehicle Traction Systems Based on Knowledge Graph Reasoning
1
School of Urban Railway Transportation, Shanghai University of Engineering Science, China
2
Higher vocational and Technical College, Shanghai University of Engineering Science, China
Submission date: 2024-04-15
Final revision date: 2024-06-24
Acceptance date: 2024-08-08
Online publication date: 2024-08-11
Publication date: 2024-08-11
Corresponding author
Qianwen Zhong
School of Urban Railway Transportation, Shanghai University of Engineering Science, China
School of Urban Railway Transportation, Shanghai University of Engineering Science, China
Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(1):192171
HIGHLIGHTS
- Propose a novel method based on knowledge graph to handle fragmented fault text data capturing multi-level associations, conducting knowledge inference based on graph structure.
- RGCN-GAT adjusts node weights dynamically to uncover potential correlations, supporting preventive maintenance.
- BERT-BILSTM-CRF enables global semantic sharing, offering interpretable fault analysis by integrating knowledge graphs and Bayesian probability.
KEYWORDS
TOPICS
ABSTRACT
Fault text records provide detailed information on faults and handling steps, which are valuable for fault analysis. However, the different individuals’ recording styles can lead to ambiguities, and it is challenging to uncover potential fault associations in complex systems. To address these issues, this paper proposes a novel method for fault information extraction and analysis. Firstly, to tackle the problem of ambiguous boundaries between entities in fault texts, an integration algorithm is employed to accurately recognize fault entities considering contextual semantic features to establish fault knowledge graph (FKG). Then, a Relational Graph Convolutional Networks (RGCN) is improved with Graph Attention Networks (GAT) for sparse nodes caused by specific types of faults, to dynamically adjust the weight distribution of node learning, inferring potential links within the graph. The proposed method was validated using actual fault records from the traction system of rail transit vehicles, and contributes a reference for the mining and analysis of fault records in complex systems.
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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