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Data Compression Method Based on Operation Condition Identification
Dong JY(董静雅)1,2,3; Fu DZ(付殿峥)1,2; Zheng ZY(郑泽宇)1,2; Liu Z(刘智)1,2,3; Gao Y(高原)1,2,3; Gui J(桂珺)1,2,3; Dong JY(董静雅)4,5,6; Fu DZ(付殿峥)4,5; Zheng ZY(郑泽宇)4,5; Liu Z(刘智)4,5,6; Gao Y(高原)4,5,6; Gui J(桂珺)4,5,6
Department数字工厂研究室
Conference Name2nd International Conference on Electrical, Communication and Computer Engineering, ICECCE 2020
Conference DateJune 12-13, 2020
Conference PlaceIstanbul, Turkey
Source Publication2nd International Conference on Electrical, Communication and Computer Engineering, ICECCE 2020
PublisherIEEE
Publication PlaceNew York
2020
Indexed ByEI
EI Accession number20204109306462
Contribution Rank1
ISBN978-1-7281-7116-6
Keyworddata compression data storage operation condition identification time series
AbstractIn response to the requirement of a large amount of historical data storage and the difficulty of data analysis and visualization in the industrial equipment data acquisition system, a data compression method based on operation condition identification is proposed in this paper. The method firstly sets the dead zone limit value for each dimensional time series according to the change of multi-dimensional time series data collected from industrial equipment; then, on the grounds of whether each piece of data meets the dead zone limit, collecting equipment operation condition information and marking operation condition labels; finally, based on the operation condition label, the data is compressed into a data matrix composed of time and operation condition information. Our storage method has good universality and realizes efficient data storage by reducing the dimension of historical data.
Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/27714
Collection数字工厂研究室
Corresponding AuthorDong JY(董静雅); Dong JY(董静雅)
Affiliation1.Department of Digital Factory, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
3.University of Chinese Academy of Sciences, Beijing, China
4.Department of Digital Factory, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
5.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
6.University of Chinese Academy of Sciences, Beijing, China
Recommended Citation
GB/T 7714
Dong JY,Fu DZ,Zheng ZY,et al. Data Compression Method Based on Operation Condition Identification[C]. New York:IEEE,2020.
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