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Review
. 2023 Jun 2;23(11):5295.
doi: 10.3390/s23115295.

Exploring Research on the Construction and Application of Knowledge Graphs for Aircraft Fault Diagnosis

Xilang Tang  1 , Guo Chi  2 , Lijie Cui  1 , Andrew W H Ip  3 , Kai Leung Yung  3 , Xiaoyue Xie  1
Affiliations
Review

Exploring Research on the Construction and Application of Knowledge Graphs for Aircraft Fault Diagnosis

Xilang Tang et al. Sensors (Basel). .

Abstract

Fault diagnosis is crucial for repairing aircraft and ensuring their proper functioning. However, with the higher complexity of aircraft, some traditional diagnosis methods that rely on experience are becoming less effective. Therefore, this paper explores the construction and application of an aircraft fault knowledge graph to improve the efficiency of fault diagnosis for maintenance engineers. Firstly, this paper analyzes the knowledge elements required for aircraft fault diagnosis, and defines a schema layer of a fault knowledge graph. Secondly, with deep learning as the main method and heuristic rules as the auxiliary method, fault knowledge is extracted from structured and unstructured fault data, and a fault knowledge graph for a certain type of craft is constructed. Finally, a fault question-answering system based on a fault knowledge graph was developed, which can accurately answer questions from maintenance engineers. The practical implementation of our proposed methodology highlights how knowledge graphs provide an effective means of managing aircraft fault knowledge, ultimately assisting engineers in identifying fault roots accurately and quickly.

Keywords: aircraft fault diagnosis; deep learning; fault knowledge extraction; knowledge graph; question-answering system.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Construction and application framework of aircraft fault knowledge graphs.
Figure 2
Figure 2
Evaluation iteration method for schema design.
Figure 3
Figure 3
The framework of SP-LEAR.
Figure 4
Figure 4
The feature extraction for relationship classification.
Figure 5
Figure 5
The framework of question-answering system.
Figure 6
Figure 6
The deployment of question-answering system.
Figure 7
Figure 7
The schema of fault knowledge graph.
Figure 8
Figure 8
An example of local knowledge graph under defined schema.
Figure 9
Figure 9
Visual display of fault knowledge graph.
Figure 10
Figure 10
The application of question-answering system.
See this image and copyright information in PMC

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