Department of Engineering and Architecture, University of Parma, 43124 Parma, Italy.
Sensors (Basel). 2022 May 30;22(11):4143. doi: 10.3390/s22114143.
This work describes a structured solution that integrates digital twin models, machine-learning algorithms, and Industry 4.0 technologies (Internet of Things in particular) with the ultimate aim of detecting the presence of anomalies in the functioning of industrial systems. The proposed solution has been designed to be suitable for implementation in industrial plants not directly designed for Industry 4.0 applications. More precisely, this manuscript delineates an approach for implementing three machine-learning algorithms into a digital twin environment and then applying them to a real plant. This paper is based on two previous studies in which the digital twin environment was first developed for the industrial plant under investigation, and then used for monitoring selected plant parameters. Findings from the previous studies are exploited in this work and advanced by implementing and testing the machine-learning algorithms. The results show that two out of the three machine-learning algorithms are effective enough in predicting anomalies, thus suggesting their implementation for enhancing the safety of employees working at industrial plants.
这项工作描述了一种结构化的解决方案，该方案将数字孪生模型、机器学习算法和工业 4.0 技术（特别是物联网）集成在一起，目的是检测工业系统运行中异常的存在。所提出的解决方案旨在适用于并非专为工业 4.0 应用而设计的工业工厂实施。更确切地说，本文阐述了一种将三种机器学习算法应用于数字孪生环境的方法，然后将其应用于实际工厂。本论文基于之前的两项研究，其中首先为所研究的工业工厂开发了数字孪生环境，然后用于监测选定的工厂参数。本工作利用了之前研究的结果，并通过实现和测试机器学习算法进行了推进。结果表明，三种机器学习算法中有两种足以有效预测异常，因此建议将其用于提高在工业工厂工作的员工的安全性。
