Work

Analysis of Fault Events in Rail Transit Vehicle Traction Systems Based on Knowledge Graph Reasoning

Year: 2024

Type: article

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.

Source: Eksploatacja i Niezawodnosc - Maintenance and Reliability

DOI: 10.17531/ein/192171

Authors: Zhao Shi, Qianwen Zhong, Shubin Zheng, Jing Wen, Lele Peng

Institution: Shanghai University of Engineering Science

Language: English

Topic: Transportation Systems and Safety

Subfield: Computer Graphics and Computer-Aided Design

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $442