Intelligent manufacturing production line data monitoring system for industrial internet of things

2020 ◽  
Vol 151 ◽  
pp. 31-41 ◽  
Author(s):  
Wei Chen
Keyword(s):  
Internet Of Things ◽  
Monitoring System ◽  
Production Line ◽  
Intelligent Manufacturing ◽  
Data Monitoring ◽  
Industrial Internet Of Things ◽  
Industrial Internet

Industrial internet of things for smart manufacturing applications using hierarchical trustful resource assignment

Work ◽  
2021 ◽  
pp. 1-11
Author(s):  
Duan Pingli ◽  
Bala Anand Muthu ◽  
Seifedine Nimer Kadry
Keyword(s):  
Internet Of Things ◽  
Manufacturing Industry ◽  
Production Systems ◽  
Intelligent Manufacturing ◽  
Smart Manufacturing ◽  
The Internet ◽  
Resource Assignment ◽  
Industrial Internet Of Things ◽  
Assignment Method ◽  
Industrial Internet

BACKGROUND: The manufacturing industry undergoes a new age, with significant changes taking place on several fronts. Companies devoted to digital transformation take their future plants inspired by the Internet of Things (IoT). The IoT is a worldwide network of interrelated physical devices, which is an essential component of the internet, including sensors, actuators, smart apps, computers, mechanical machines, and people. The effective allocation of the computing resources and the carrier is critical in the industrial internet of Things (IIoT) for smart production systems. Indeed, the existing assignment method in the smart production system cannot guarantee that resources meet the inherently complex and volatile requirements of the user are timely. Many research results on resource allocations in auction formats which have been implemented to consider the demand and real-time supply for smart development resources, but safety privacy and trust estimation issues related to these outcomes are not actively discussed. OBJECTIVES: The paper proposes a Hierarchical Trustful Resource Assignment (HTRA) and Trust Computing Algorithm (TCA) based on Vickrey Clarke-Groves (VGCs) in the computer carriers necessary resources to communicate wirelessly among IIoT devices and gateways, and the allocation of CPU resources for processing information at the CPC. RESULTS: Finally, experimental findings demonstrate that when the IIoT equipment and gateways are valid, the utilities of each participant are improved. CONCLUSION: This is an easy and powerful method to guarantee that intelligent manufacturing components genuinely work for their purposes, which want to integrate each element into a system without interactions with each other.

Design of industrial internet of things system based on machine learning and artificial intelligence technology

2020 ◽  
pp. 1-11
Author(s):  
Xu Kun ◽  
Zhiliang Wang ◽  
Ziang Zhou ◽  
Wang Qi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Internet Of Things ◽  
Monitoring System ◽  
Remote Monitoring ◽  
Industrial Equipment ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Test Platform ◽  
Artificial Intelligence Technology

For industrial production, the traditional manual on-site monitoring method is far from meeting production needs, so it is imperative to establish a remote monitoring system for equipment. Based on machine learning algorithms, this paper combines artificial intelligence technology and Internet of Things technology to build an efficient, fast, and accurate industrial equipment monitoring system. Moreover, in view of the characteristics of the diverse types of equipment, scattered layout, and many parameters in the manufacturing equipment as well as the complexity of the high temperature, high pressure, and chemical environment in which the equipment is located, this study designs and implements a remote monitoring and data analysis system for industrial equipment based on the Internet of Things. In addition, based on the application scenarios of the actual aeronautical weather floating platform test platform, this study combines the platform prototype system to design and implement a set of strong real-time communication test platform based on the Windows operating system. The test results show that the industrial Internet of Things system based on machine learning and artificial intelligence technology constructed in this paper has certain practicality.

scholarly journals Smart Machinery Monitoring System with Reduced Information Transmission and Fault Prediction Methods Using Industrial Internet of Things

Mathematics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 3
Author(s):  
Ming-Fong Tsai ◽  
Yen-Ching Chu ◽  
Min-Hao Li ◽  
Lien-Wu Chen
Keyword(s):  
Internet Of Things ◽  
Monitoring System ◽  
Fault Prediction ◽  
Learning Technology ◽  
Industrial Internet Of Things ◽  
Machine Error ◽  
Industrial Internet ◽  
Cloud Server ◽  
Transmission Speed ◽  
Monitoring Platform

A monitoring system for smart machinery has been considered to be one of the most important goals in recent enterprises. This monitoring system will encounter huge difficulties, such as more data uploaded by smart machines, and the available internet bandwidth will influence the transmission speed of data and the reliability of the equipment monitoring platform. This paper proposes reducing the periodical information that has been uploaded to the monitoring platform by setting an upload event through the traits of production data from machines. The proposed methods reduce bandwidth and power consumption. The monitoring information is reconstructed by the proposed methods, so history data will not reduce storage in the cloud server database. In order to reduce the halt time caused by machine error, the proposed system uses machine-learning technology to model the operating status of machinery for fault prediction. In the experimental results, the smart machinery monitoring system using the Industrial Internet of Things reduces the volume of information uploaded by 54.57% and obtains a 98% prediction accuracy.

scholarly journals Development of the production monitoring system based on the Industrial Internet of Things platform

2021 ◽  
Vol 1753 (1) ◽  
pp. 012033
Author(s):  
A B Mikhailov ◽  
S D Tretyakov ◽  
Y S Andreev ◽  
V M Medunetskiy ◽  
Ko Aung Thinn
Keyword(s):  
Internet Of Things ◽  
Monitoring System ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Production Monitoring
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