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基于机器学习和物联网的可靠工业 4.0，用于分析、监控和保护智能电表。

Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters.

机构信息

Industry 4.0 Implementation Center, Center for Cyber-Physical System Innovation, National Taiwan University of Science and Technology, Taipei 10607, Taiwan.

Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo 11629, Egypt.

出版信息

Sensors (Basel). 2021 Jan 12;21(2):487. doi: 10.3390/s21020487.

DOI:10.3390/s21020487
PMID:33445540
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7828067/
Abstract

The modern control infrastructure that manages and monitors the communication between the smart machines represents the most effective way to increase the efficiency of the industrial environment, such as smart grids. The cyber-physical systems utilize the embedded software and internet to connect and control the smart machines that are addressed by the internet of things (IoT). These cyber-physical systems are the basis of the fourth industrial revolution which is indexed by industry 4.0. In particular, industry 4.0 relies heavily on the IoT and smart sensors such as smart energy meters. The reliability and security represent the main challenges that face the industry 4.0 implementation. This paper introduces a new infrastructure based on machine learning to analyze and monitor the output data of the smart meters to investigate if this data is real data or fake. The fake data are due to the hacking and the inefficient meters. The industrial environment affects the efficiency of the meters by temperature, humidity, and noise signals. Furthermore, the proposed infrastructure validates the amount of data loss via communication channels and the internet connection. The decision tree is utilized as an effective machine learning algorithm to carry out both regression and classification for the meters' data. The data monitoring is carried based on the industrial digital twins' platform. The proposed infrastructure results provide a reliable and effective industrial decision that enhances the investments in industry 4.0.

摘要

管理和监控智能机器之间通信的现代控制基础设施代表了提高工业环境效率的最有效方法，例如智能电网。 这些网络物理系统利用嵌入式软件和互联网连接和控制物联网（IoT）所涉及的智能机器。 这些网络物理系统是第四次工业革命的基础，第四次工业革命以工业 4.0 为指标。 特别是，工业 4.0 严重依赖物联网和智能传感器，例如智能电表。 可靠性和安全性是工业 4.0 实施所面临的主要挑战。 本文提出了一种基于机器学习的新基础设施，用于分析和监控智能电表的输出数据，以调查这些数据是真实数据还是伪造数据。 伪造数据是由于黑客攻击和电表效率低下造成的。 工业环境会通过温度、湿度和噪声信号影响电表的效率。 此外，所提出的基础设施通过通信通道和互联网连接验证数据丢失量。 决策树被用作有效的机器学习算法，为电表数据执行回归和分类。 数据监控基于工业数字双胞胎平台进行。 所提出的基础设施结果提供了可靠有效的工业决策，从而增强了对工业 4.0 的投资。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0ad/7828067/b640bd410ad5/sensors-21-00487-g013.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0ad/7828067/b640bd410ad5/sensors-21-00487-g013.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0ad/7828067/3877341b0340/sensors-21-00487-g002.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0ad/7828067/4476f9dba051/sensors-21-00487-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0ad/7828067/137cc3a4ec17/sensors-21-00487-g006a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0ad/7828067/304ccc7de5f6/sensors-21-00487-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0ad/7828067/7ba9ce0b5a4e/sensors-21-00487-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0ad/7828067/5bba6d5c25c0/sensors-21-00487-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0ad/7828067/0d45b469b6a5/sensors-21-00487-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0ad/7828067/bb5c294734e6/sensors-21-00487-g011.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0ad/7828067/b640bd410ad5/sensors-21-00487-g013.jpg

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