research-article

Construction and Application of the Knowledge Graph Method in Maintenance of Robot in Automotive Manufacturing Industry

Authors:

Yan ZhangAuthors Info & Claims

ISIA '23: Proceedings of the 2023 International Conference on Intelligent Sensing and Industrial Automation

Article No.: 15, Pages 1 - 9

https://doi.org/10.1145/3632314.3632332

Published: 09 December 2023 Publication History

ISIA '23: Proceedings of the 2023 International Conference on Intelligent Sensing and Industrial Automation

Construction and Application of the Knowledge Graph Method in Maintenance of Robot in Automotive Manufacturing Industry

Pages 1 - 9

Abstract
References

Abstract

Based on the spare parts and structure data of industrial robots, the entity list of robot parts is established to form a query dictionary, and entity annotation is performed on the robot corpus by means of dictionary query, which reduces the cost of manual annotation and ensures the quality of annotation data. In the process of entity recognition training, Bert+Bilstm+CRF model structure is used to initially use 70% of the dictionary data for annotation, and the model is trained by iteratively increasing the annotation data in a continuous cycle, so that the model can extract all the entities in the robot corpus as much as possible. In addition, the material number/model information and PM maintenance content/strategy of the entity have been used as attributes of the entity. Meanwhile, the experience summarized by the failure model and effect analysis of industrial robots is fully utilized to connect the phenomena, causes and measures through the entities in order to build the industrial robot knowledge graph relationships. The constructed knowledge graph relationship is stored in a Neo4j graphical database, making it convenient for content retrieval and inquiry of application systems.In the industrial robot knowledge graph application side, the field maintenance personnel requirements are collected through a questionnaire survey and the requirements are classified into intent. A Bert+TextCNN structure model is built to realize the intention recognition of user inquiries. By combining entity recognition models and intent classification models, the system is able to better understand user inquiry needs, leading to the implementation of an intelligent maintenance system for industrial robots.

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Cited By

Xu YPeng TTao JBai AZhang NLim K(2024)A representation learning-based approach to enhancing manufacturing quality for low-voltage electrical productsAdvanced Engineering Informatics10.1016/j.aei.2024.10263662(102636)Online publication date: Oct-2024
https://doi.org/10.1016/j.aei.2024.102636

Index terms have been assigned to the content through auto-classification.

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ISIA '23: Proceedings of the 2023 International Conference on Intelligent Sensing and Industrial Automation

December 2023

292 pages

ISBN:9798400709401

DOI:10.1145/3632314

Copyright © 2023 ACM.

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Published: 09 December 2023

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ISIA 2023

ISIA 2023: 2023 International Conference on Intelligent Sensing and Industrial Automation

December 9 - 10, 2023

Virtual Event, China

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Cited By

Xu YPeng TTao JBai AZhang NLim K(2024)A representation learning-based approach to enhancing manufacturing quality for low-voltage electrical productsAdvanced Engineering Informatics10.1016/j.aei.2024.10263662(102636)Online publication date: Oct-2024
https://doi.org/10.1016/j.aei.2024.102636

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