Knowledge Graph Construction Based on a Joint Model for Equipment Maintenance

Ping Lou, Dan Yu, Xuemei Jiang (), Jiwei Hu, Yuhang Zeng and Chuannian Fan
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Ping Lou: School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
Dan Yu: School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
Xuemei Jiang: School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
Jiwei Hu: School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
Yuhang Zeng: School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
Chuannian Fan: School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China

Mathematics, 2023, vol. 11, issue 17, 1-23

Abstract: Under the background of intelligent manufacturing, industrial systems are developing in a more complex and intelligent direction. Equipment maintenance management is facing significant challenges in terms of maintenance workload, system reliability and stability requirements and the overall skill requirements of maintenance personnel. Equipment maintenance management is also developing in the direction of intellectualization. It is important to have a method to construct a domain knowledge graph and to organize and utilize it. As is well known, traditional equipment maintenance is mainly dependent on technicians, and they are required to be very familiar with the maintenance manuals. But it is very difficult to manage and exploit a large quantity of knowledge for technicians in a short time. Hence a method to construct a knowledge graph (KG) for equipment maintenance is proposed to extract knowledge from manuals, and an effective maintenance scheme is obtained with this knowledge graph. Firstly, a joint model based on an enhanced BERT-Bi-LSTM-CRF is put forward to extract knowledge automatically, and a Cosine and Inverse Document Frequency (IDF) based on semantic similarity a presented to eliminate redundancy in the process of the knowledge fusion. Finally, a Decision Support System (DSS) for equipment maintenance is developed and implemented, in which knowledge can be extracted automatically and provide an equipment maintenance scheme according to the requirements. The experimental results show that the joint model used in this paper performs well on Chinese text related to equipment maintenance, with an F1 score of 0.847. The quality of the knowledge graph constructed after eliminating redundancy is also significantly improved.

Keywords: knowledge graph; natural language processing; semantic similarity; BERT-Bi-LSTM-CRF (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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