Joint Knowledge Graph and Large Language Model for Fault Diagnosis and Its Application in Aviation Assembly

In complex assembly industry settings, fault localization involves rapidly and accurately identifying the source of a fault and obtaining a troubleshooting solution based on fault symptoms. This study proposes a knowledge-enhanced joint model that incorporates aviation assembly knowledge graph (KG)...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on industrial informatics 2024-06, Vol.20 (6), p.8160-8169
Main Authors: LIU, Peifeng, Qian, Lu, Zhao, Xingwei, Tao, Bo
Format: Article
Language:English
Subjects:
Assembly
Aviation
Cognition
Data-driven
Embedding
Fault diagnosis
fault localization
Fault location
Functional testing
Graph theory
Hidden Markov models
intelligent fault diagnosis
Knowledge engineering
knowledge graph (KG)
Knowledge graphs
Knowledge representation
large language model (LLM)
Large language models
Reconfiguration
Task analysis
Training
Troubleshooting
Tuning
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In complex assembly industry settings, fault localization involves rapidly and accurately identifying the source of a fault and obtaining a troubleshooting solution based on fault symptoms. This study proposes a knowledge-enhanced joint model that incorporates aviation assembly knowledge graph (KG) embedding into large language models (LLMs). This model utilizes graph-structured Big Data within KGs to conduct prefix-tuning of the LLMs. The KGs for prefix-tuning enable an online reconfiguration of the LLMs, which avoids a massive computational load. Through the subgraph embedding learning process, the specialized knowledge of the joint model within the aviation assembly domain, especially in fault localization, is strengthened. In the context of aviation assembly functional testing, the joint model can generate knowledge subgraphs, fuse knowledge through retrieval augmentation, and ultimately provide knowledge-based reasoning responses. In practical industrial scenario experiments, the joint enhancement model demonstrates an accuracy of 98.5% for fault diagnosis and troubleshooting schemes.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2024.3366977