ZHANG Bin, ZHU Jian-tao, XU Zhao. Data Gathering Optimization Strategy Based on Dynamic Frequency Algorithm for Remote Monitoring System[J]. 2016, 33(8): 86-91.
ZHANG Bin, ZHU Jian-tao, XU Zhao. Data Gathering Optimization Strategy Based on Dynamic Frequency Algorithm for Remote Monitoring System[J]. 2016, 33(8): 86-91.DOI:
The paper proposes an algorithm based dynamic frequency to solve the problem. Base the prediction of ARMA and data changing trend
the algorithm changes the frequency of data collection. Through the algorithm
the key information could be collected in time and the efficiency could be improved. At last
the algorithm and experiment will be displayed and the benefits of the algorithm in comparison with polling have been shown.
关键词
Keywords
references
李向前. 复杂装备故障预测与健康管理关键技术研究[D]. 北京: 北京理工大学, 2014.
Youqiang Sun. Prediction about time series based on updated prediction ARMA Model[C]//International Conference on Fuzzy System and Knowledge Discovery. Shenyang: IEEE, 2013: 680-684.
王衍学. 机械故障监测诊断的若干新方法及其应用研究[D]. 西安: 西安交通大学2009.
杨叔子. 时间序列分析的工程应用[M]. 武汉: 华中科技大学出版社, 2007.
Chao Wu. Ambient Singals based Power System Oscillation Modes Identification considering Model Order Selection[C]// Power % Energy Society General Meeting. Calgary, AB: IEEE, 2009: 1-7.
Pappas S S, Ekonomou L, Karamousantas D Ch, et al. Electricity demand loads modeling using Auto Regressive Moving Average (ARMA) models [J]. Energy, 2008, 33(9): 1353-1360. DOI: 1353