Large language model assisted fine-grained knowledge graph construction for robotic fault diagnosis

Liao, Xingming, Chen, Chong, Wang, Zhuowei, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940, Wang, Tao and Cheng, Lianglun 2025. Large language model assisted fine-grained knowledge graph construction for robotic fault diagnosis. Advanced Engineering Informatics 65 (Part A) , 103134. 10.1016/j.aei.2025.103134 Item availability restricted.

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Abstract

With the rapid deployment of industrial robots in manufacturing, the demand for advanced maintenance techniques to sustain operational efficiency has become crucial. Fault diagnosis Knowledge Graph (KG) is essential as it interlinks multi-source data related to industrial robot faults, capturing multi-level semantic associations among different fault events. However, the construction and application of fine-grained fault diagnosis KG face significant challenges due to the inherent complexity of nested entities in maintenance texts and the severe scarcity of annotated industrial data. In this study, we propose a Large Language Model (LLM) assisted data augmentation approach, which handles the complex nested entities in maintenance corpora and constructs a more fine-grained fault diagnosis KG. Firstly, the fine-grained ontology is constructed via LLM Assistance in Industrial Nested Named Entity Recognition (assInNNER). Then, an Industrial Nested Label Classification Template (INCT) is designed, enabling the use of nested entities in Attention-map aware keyword selection for the Industrial Nested Language Model (ANLM) data augmentation methods. ANLM can effectively improve the model’s performance in nested entity extraction when corpora are scarce. Subsequently, a Confidence Filtering Mechanism (CFM) is introduced to evaluate and select the generated data for enhancement, and assInNNER is further deployed to recall the negative samples corpus again to further improve performance. Experimental studies based on multi-source corpora demonstrate that compared to existing algorithms, our method achieves an average F1 increase of 8.25 %, 3.31 %, and 1.96 % in 5%, 10 %, and 25 % in few-shot settings, respectively.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	1474-0346
Date of First Compliant Deposit:	22 January 2025
Date of Acceptance:	14 January 2025
Last Modified:	29 Jan 2025 15:15
URI:	https://orca.cardiff.ac.uk/id/eprint/175513

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