A Novel Technique of Synthetic Data Generation for Asset Administration Shells in Industry 4.0 Scenarios

作     者：De, Suman Mitra, Pabitra 

作者机构：IIT Kharagpur Centre for Computational and Data Science West Bengal 721302 India SAP Labs Bangalore560066 India 

出 版 物：《IEEE Transactions on Artificial Intelligence》 (IEEE. Trans. Artif. Intell.)

年 卷 期：2024年第5卷第10期

页      面：5258-5266页

核心收录：

主　　题：Knowledge management 

摘      要：Manufacturing plants are highly dependent on machines and involve a significant number of equipment to produce a finished product. Industry 4.0 helps structure the processes involved in such setups and enables the functionalities of how the equipment and machines interact with each other. With the advancement of visualizing these types of equipment as digital twins, multiple opportunities have developed for automating processes and optimizing various aspects of the assembly, especially for original equipment manufacturers (OEMs). One problem that concerns a network of manufacturers is the availability of equipment and spare parts data which are sometimes confidential but are required by a new member in the network for several analytical applications. This article looks at this problem statement to turn this into an opportunity by introducing a novel concept of AASGAN that combines the knowledge representation of a digital twin data in the asset administration shell (AAS) and a synthetic data generation technique of generative adversarial network (GAN) to generate fake data that is identical to real data. This article also explains how this concept helps perform analytical operations using industry grade solutions for the automotive industry available for managing digital twins and other scenarios for industrial automation. © 2020 IEEE.