Affiliations: [a] School of Computer & Communication Engineering, University of Science and Technology Beijing, Beijing, China
| [b] School of Mechanical and Electrical Engineering, Zhoukou Normal University, Zhoukou, Henan, China
| [c] School of Network Engineering, Zhoukou Normal University, ZhouKou, Henan, China
Correspondence:
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Corresponding author. Xu Kun, University of Science and Technology Beijing, E-mail: [email protected].
Abstract: 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.
Keywords: Machine learning, artificial intelligence, industry, internet of things
