Article: Intelligent factory perception ability using distributed knowledge graph Journal: International Journal of Data Science (IJDS) 2024 Vol.9 No.3/4 pp.203 - 221 Abstract: Traditional research often faces the problem of information segregation, resulting in a lack of access to comprehensive, cross-domain data during the decision-making process, limiting a comprehensive understanding of the entire smart factory ecosystem. In this paper, we introduce the proximal policy optimisation (PPO) algorithm, combined with the inference capability of knowledge graph, to support complex decision-making problems in smart factories. In this paper, we collected smart factory data from different departments and constructed a distributed knowledge graph, defined semantic labels for entities and relationships, and mapped data from different data sources into the semantic model of the knowledge graph, built a decision network using multilayer perceptron, and updated the parameters of the policy network through PPO. The experimental results show that the average fault prediction accuracy of PPO combined with distributed knowledge graph reaches 96.1%, and the fluctuation of fault prediction accuracy within 12 months is only 0.1%. Inderscience Publishers - linking academia, business and industry through research

Title: Intelligent factory perception ability using distributed knowledge graph

Authors: Wenjuan Wang; Donghui Shen; Anyin Bao; Jianming Shao; Shunkai Sun

Addresses: China Tobacco Zhejiang Industrial Co. Ltd., Hangzhou, 315504, Zhejiang, China ' China Tobacco Zhejiang Industrial Co. Ltd., Hangzhou, 315504, Zhejiang, China ' China Tobacco Zhejiang Industrial Co. Ltd., Hangzhou, 315504, Zhejiang, China ' China Tobacco Zhejiang Industrial Co. Ltd., Hangzhou, 315504, Zhejiang, China ' China Tobacco Zhejiang Industrial Co. Ltd., Hangzhou, 315504, Zhejiang, China

Abstract: Traditional research often faces the problem of information segregation, resulting in a lack of access to comprehensive, cross-domain data during the decision-making process, limiting a comprehensive understanding of the entire smart factory ecosystem. In this paper, we introduce the proximal policy optimisation (PPO) algorithm, combined with the inference capability of knowledge graph, to support complex decision-making problems in smart factories. In this paper, we collected smart factory data from different departments and constructed a distributed knowledge graph, defined semantic labels for entities and relationships, and mapped data from different data sources into the semantic model of the knowledge graph, built a decision network using multilayer perceptron, and updated the parameters of the policy network through PPO. The experimental results show that the average fault prediction accuracy of PPO combined with distributed knowledge graph reaches 96.1%, and the fluctuation of fault prediction accuracy within 12 months is only 0.1%.

Keywords: intelligent factory; perception ability; distributed knowledge graph; fault prediction; PPO; proximal policy optimisation.

DOI: 10.1504/IJDS.2024.142819

International Journal of Data Science, 2024 Vol.9 No.3/4, pp.203 - 221

Received: 11 Apr 2024
Accepted: 25 Jun 2024
Published online: 23 Nov 2024 *

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Title: Intelligent factory perception ability using distributed knowledge graph

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