Intelligent factory perception ability using distributed knowledge graph

Wenjuan Wang, Donghui Shen, Anyin Bao, Jianming Shao and Shunkai Sun

International Journal of Data Science, 2024, vol. 9, issue 3/4, 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%.

Keywords: intelligent factory; perception ability; distributed knowledge graph; fault prediction; PPO; proximal policy optimisation. (search for similar items in EconPapers)
Date: 2024
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