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  • 1Aviation Engineering School, Air Force Engineering University, Xi'an 710038, China.

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Summary
This summary is machine-generated.

This study introduces a knowledge graph to enhance aircraft health management by improving fault data correlation and troubleshooting. The developed platform supports rapid knowledge graph construction and diagnosis for better aircraft maintenance.

Keywords:
PHMQ&A systemjoint extraction of entity relationshipsknowledge graph

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Area of Science:

  • Aerospace Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Traditional aircraft health management relies heavily on experience, leading to issues with troubleshooting robustness and slow response times.
  • Existing methods struggle to effectively correlate fault data and represent maintenance knowledge.
  • There is a need for advanced systems to manage and analyze aircraft health data efficiently.

Purpose of the Study:

  • To propose a novel knowledge graph approach for aircraft health management.
  • To improve the correlation of fault data by representing maintenance experience.
  • To develop a platform for efficient knowledge graph construction and fault diagnosis.

Main Methods:

  • Utilized flight control system manuals and typical fault cases as data sources.
  • Employed an ERNIE model-based method for data classification.
  • Implemented a joint entity relationship extraction model (ERNIE-BiLSTM-CRF-TreeBiLSTM) for improved accuracy and reduced semantic complexity.
  • Developed a dedicated knowledge graph platform with modules for text classification, knowledge extraction, auditing, Q&A, and visualization.

Main Results:

  • Successfully constructed a fault knowledge graph for aircraft health management.
  • The proposed entity relationship extraction model demonstrated enhanced accuracy.
  • The developed platform facilitates efficient management of aircraft health data.
  • The system provides a foundation for rapid knowledge graph construction and knowledge graph-based fault diagnosis.

Conclusions:

  • The knowledge graph approach offers a robust solution to limitations in traditional aircraft health management.
  • The developed platform effectively supports the creation and utilization of fault knowledge graphs.
  • This methodology enhances fault diagnosis capabilities and improves overall aircraft maintenance efficiency.