A Benchmark Dataset for Industry 4.0 Production Line and Generation of Knowledge Graphs

Tracking #: 3198-4412

This paper is currently under review
Authors: 
Muhammad Yahya
Aabid Ali
Qaiser Mehmood1
Lan Yang
John Breslin
Muhammad Intizar Ali

Responsible editor: 
Guest Editors SW for Industrial Engineering 2022

Submission type: 
Dataset Description
Abstract: 
Industry 4.0 (I4.0) is a new era in the industrial revolution that emphasizes machine connectivity, automation, and data analytics. The I4.0 pillars such as autonomous robots, cloud computing, horizontal and vertical system integration, and industrial internet of things have increased the performance and efficiency of production lines in the manufacturing industry. Over the past years, efforts have been made to propose semantic models to represent the manufacturing domain knowledge, one such model is Reference Generalized Ontological Model (RGOM). However, its adaptability like other models is not ensured due to the lack of manufacturing data. In this paper, we aim to develop an I4.0 benchmark dataset that can be used to validate the tools, techniques, and methods. This work is a result of collaborations with the production line managers, supervisors, and engineers of a football industry to acquire realistic production line data1. Knowledge Graphs or Knowledge Graph (KG) have emerged as a significant technology to store the semantics of the domain entities. It has been used in a variety of industries, including banking, the automobile industry, oil and gas, pharmaceutical and health care, publishing, media, etc. The data is mapped with RGOM classes and relations using an automated solution based on JenaAPI, producing an I4.0 KG . It contains more than 2.5 million axioms and about 1 million instances. This KG enables us to demonstrate the adaptability and usefulness of the RGOM. Our research helps the production line staff to take timely decisions by exploiting the information embedded in the KG. In relation to this, the RGOM adaptability is demonstrated with the help of a use case scenario to discover required information such as current temperature at a particular time, status of the motor, tools deployed on the machine, etc.
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Under Review