A Literature Review Research on Monitoring Conditions of Mechanical Equipment Based on Edge Computing. (8th October 2022)

• Record Type: Journal Article
• Authors: Song, Liqiang; Wang, Huaiguang; Shi, Zhiyong
• Other Names: Liu Ye Academic Editor.
• Is Part Of: Applied bionics and biomechanics. Volume 2022(2022)
• Publication Date: 2022-10-08
• Subjects: Bionics -- Periodicals; Biomechanics -- Periodicals; Biomedical engineering -- Periodicals; 003.505
• DOI: 10.1155/2022/9489306
• Languages: English
• ISSNs: 1176-2322

Abstract

Abstract : The motivation of this research is to review all methods used in data compression of collected data in monitoring the condition of equipment based on the framework of edge computing. Since a large amount of signal data is collected when monitoring conditions of mechanical equipment, namely, signals of running machines are continuously transmitted to be crunched, compressed data should be handled effectively. However, this process occupies resources since data transmission requires the allocation of a large capacity. To resolve this problem, this article examines the monitoring conditions of equipment based on edge computing. First, the signal is pre-processed by edge computing, so that the fault characteristics can be identified quickly. Second, signals with difficult-to-identify fault characteristics need to be compressed to save transmission resources. Then, different types of signal data collected in mechanical equipment conditions are compressed by various compression methods and uploaded to the cloud. Finally, the cloud platform, which has powerful processing capability, is processed to improve the volume of the data transmission. By examining and analyzing the monitoring conditions and signal compression methods of mechanical equipment, the future development trend is elaborated to provide references and ideas for the contemporary research of data monitoring and data compression algorithms. Consequently, the manuscript presents different compression methods in detail and clarifies the data compression methods used for the signal compression of equipment based on edge computing.